idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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4,401 | Mathematical Statistics Videos | The folks at SLAC put videos of their lecture series online. Given that their audience is mostly physicists, they tend to be fairly mathematical.
SLUO Lecture Series (see the "Stat" links) | Mathematical Statistics Videos | The folks at SLAC put videos of their lecture series online. Given that their audience is mostly physicists, they tend to be fairly mathematical.
SLUO Lecture Series (see the "Stat" links) | Mathematical Statistics Videos
The folks at SLAC put videos of their lecture series online. Given that their audience is mostly physicists, they tend to be fairly mathematical.
SLUO Lecture Series (see the "Stat" links) | Mathematical Statistics Videos
The folks at SLAC put videos of their lecture series online. Given that their audience is mostly physicists, they tend to be fairly mathematical.
SLUO Lecture Series (see the "Stat" links) |
4,402 | Mathematical Statistics Videos | There is one called Math and probability for life sciences, but I haven't followed it so I can't tell you if its good or not. | Mathematical Statistics Videos | There is one called Math and probability for life sciences, but I haven't followed it so I can't tell you if its good or not. | Mathematical Statistics Videos
There is one called Math and probability for life sciences, but I haven't followed it so I can't tell you if its good or not. | Mathematical Statistics Videos
There is one called Math and probability for life sciences, but I haven't followed it so I can't tell you if its good or not. |
4,403 | Mathematical Statistics Videos | This site from Ecole normal Supérieure de Paris contains a lot of very interesting video
http://www.diffusion.ens.fr/index.php?res=themes&idtheme=30
I greatly encourage you to visit this site !!
Among other you will find there all video presentation from the conference "Mathematical Foundations of Learning Theory" th... | Mathematical Statistics Videos | This site from Ecole normal Supérieure de Paris contains a lot of very interesting video
http://www.diffusion.ens.fr/index.php?res=themes&idtheme=30
I greatly encourage you to visit this site !!
Amo | Mathematical Statistics Videos
This site from Ecole normal Supérieure de Paris contains a lot of very interesting video
http://www.diffusion.ens.fr/index.php?res=themes&idtheme=30
I greatly encourage you to visit this site !!
Among other you will find there all video presentation from the conference "Mathematical Fou... | Mathematical Statistics Videos
This site from Ecole normal Supérieure de Paris contains a lot of very interesting video
http://www.diffusion.ens.fr/index.php?res=themes&idtheme=30
I greatly encourage you to visit this site !!
Amo |
4,404 | Mathematical Statistics Videos | I do not know at what level you want the videos to be but I have heard good things about Khan's Academy: http://www.khanacademy.org/#Statistics | Mathematical Statistics Videos | I do not know at what level you want the videos to be but I have heard good things about Khan's Academy: http://www.khanacademy.org/#Statistics | Mathematical Statistics Videos
I do not know at what level you want the videos to be but I have heard good things about Khan's Academy: http://www.khanacademy.org/#Statistics | Mathematical Statistics Videos
I do not know at what level you want the videos to be but I have heard good things about Khan's Academy: http://www.khanacademy.org/#Statistics |
4,405 | Mathematical Statistics Videos | Many of the Berkeley introductory statistics courses are available online (and on iTunes). Here's an example: Stats 2. You can find more here. | Mathematical Statistics Videos | Many of the Berkeley introductory statistics courses are available online (and on iTunes). Here's an example: Stats 2. You can find more here. | Mathematical Statistics Videos
Many of the Berkeley introductory statistics courses are available online (and on iTunes). Here's an example: Stats 2. You can find more here. | Mathematical Statistics Videos
Many of the Berkeley introductory statistics courses are available online (and on iTunes). Here's an example: Stats 2. You can find more here. |
4,406 | Mathematical Statistics Videos | There is a new resources forming these days for talks about R:
https://www.r-bloggers.com/RUG/
Compiled by the organizers of "R Users Groups" around the world (right now, mainly around the States).
It is a new project (just a few weeks old), but already got good content on it, and good people wanting to take part in it... | Mathematical Statistics Videos | There is a new resources forming these days for talks about R:
https://www.r-bloggers.com/RUG/
Compiled by the organizers of "R Users Groups" around the world (right now, mainly around the States).
It | Mathematical Statistics Videos
There is a new resources forming these days for talks about R:
https://www.r-bloggers.com/RUG/
Compiled by the organizers of "R Users Groups" around the world (right now, mainly around the States).
It is a new project (just a few weeks old), but already got good content on it, and good pe... | Mathematical Statistics Videos
There is a new resources forming these days for talks about R:
https://www.r-bloggers.com/RUG/
Compiled by the organizers of "R Users Groups" around the world (right now, mainly around the States).
It |
4,407 | Mathematical Statistics Videos | There are a bunch of helpful video tutorials on basic statistics & data mining with R and Weka at SentimentMining.net.
http://sentimentmining.net/StatisticsVideos/ | Mathematical Statistics Videos | There are a bunch of helpful video tutorials on basic statistics & data mining with R and Weka at SentimentMining.net.
http://sentimentmining.net/StatisticsVideos/ | Mathematical Statistics Videos
There are a bunch of helpful video tutorials on basic statistics & data mining with R and Weka at SentimentMining.net.
http://sentimentmining.net/StatisticsVideos/ | Mathematical Statistics Videos
There are a bunch of helpful video tutorials on basic statistics & data mining with R and Weka at SentimentMining.net.
http://sentimentmining.net/StatisticsVideos/ |
4,408 | Mathematical Statistics Videos | There is a series of Google Tech Talk videos called Stats 202 - Statistical Aspects of Data Mining | Mathematical Statistics Videos | There is a series of Google Tech Talk videos called Stats 202 - Statistical Aspects of Data Mining | Mathematical Statistics Videos
There is a series of Google Tech Talk videos called Stats 202 - Statistical Aspects of Data Mining | Mathematical Statistics Videos
There is a series of Google Tech Talk videos called Stats 202 - Statistical Aspects of Data Mining |
4,409 | Mathematical Statistics Videos | I found the 'Probability Primer' Lectures very useful and informative :
http://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4&feature=plcp
A series of videos giving an introduction
to some of the basic definitions, notation, and concepts one
would encounter in a 1st year graduate probability course. | Mathematical Statistics Videos | I found the 'Probability Primer' Lectures very useful and informative :
http://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4&feature=plcp
A series of videos giving an introduction
to some of | Mathematical Statistics Videos
I found the 'Probability Primer' Lectures very useful and informative :
http://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4&feature=plcp
A series of videos giving an introduction
to some of the basic definitions, notation, and concepts one
would encounter in a 1st year gradua... | Mathematical Statistics Videos
I found the 'Probability Primer' Lectures very useful and informative :
http://www.youtube.com/playlist?list=PL17567A1A3F5DB5E4&feature=plcp
A series of videos giving an introduction
to some of |
4,410 | Mathematical Statistics Videos | UCCS mathematics video archive has
archived videos from a range of courses in mathematics. Several subjects called Mathematical Statistics I and Mathematical Statistics II are available. The main site requires a free registration to access.
Slightly more accessible are the videos for a subset of the courses on the UC... | Mathematical Statistics Videos | UCCS mathematics video archive has
archived videos from a range of courses in mathematics. Several subjects called Mathematical Statistics I and Mathematical Statistics II are available. The main si | Mathematical Statistics Videos
UCCS mathematics video archive has
archived videos from a range of courses in mathematics. Several subjects called Mathematical Statistics I and Mathematical Statistics II are available. The main site requires a free registration to access.
Slightly more accessible are the videos for a ... | Mathematical Statistics Videos
UCCS mathematics video archive has
archived videos from a range of courses in mathematics. Several subjects called Mathematical Statistics I and Mathematical Statistics II are available. The main si |
4,411 | Mathematical Statistics Videos | MIT Open Courseware Discrete Stochastic Processes
Discrete stochastic processes are essentially probabilistic systems
that evolve in time via random changes occurring at discrete fixed or
random intervals. This course aims to help students acquire both the
mathematical principles and the intuition necessary to c... | Mathematical Statistics Videos | MIT Open Courseware Discrete Stochastic Processes
Discrete stochastic processes are essentially probabilistic systems
that evolve in time via random changes occurring at discrete fixed or
random | Mathematical Statistics Videos
MIT Open Courseware Discrete Stochastic Processes
Discrete stochastic processes are essentially probabilistic systems
that evolve in time via random changes occurring at discrete fixed or
random intervals. This course aims to help students acquire both the
mathematical principles a... | Mathematical Statistics Videos
MIT Open Courseware Discrete Stochastic Processes
Discrete stochastic processes are essentially probabilistic systems
that evolve in time via random changes occurring at discrete fixed or
random |
4,412 | Mathematical Statistics Videos | I just came across this website, CensusAtSchool -- Informal inference. Maybe worth looking at the videos and handouts... | Mathematical Statistics Videos | I just came across this website, CensusAtSchool -- Informal inference. Maybe worth looking at the videos and handouts... | Mathematical Statistics Videos
I just came across this website, CensusAtSchool -- Informal inference. Maybe worth looking at the videos and handouts... | Mathematical Statistics Videos
I just came across this website, CensusAtSchool -- Informal inference. Maybe worth looking at the videos and handouts... |
4,413 | Mathematical Statistics Videos | An introductory set of statistics lectures with a voice over a slide presentation.
http://www.online.math.uh.edu/Math2311/index.htm
The lecture series is elementary, but I like how the lecturer communicates clearly and shows how to speak the formulas encountered in statistics. | Mathematical Statistics Videos | An introductory set of statistics lectures with a voice over a slide presentation.
http://www.online.math.uh.edu/Math2311/index.htm
The lecture series is elementary, but I like how the lecturer commu | Mathematical Statistics Videos
An introductory set of statistics lectures with a voice over a slide presentation.
http://www.online.math.uh.edu/Math2311/index.htm
The lecture series is elementary, but I like how the lecturer communicates clearly and shows how to speak the formulas encountered in statistics. | Mathematical Statistics Videos
An introductory set of statistics lectures with a voice over a slide presentation.
http://www.online.math.uh.edu/Math2311/index.htm
The lecture series is elementary, but I like how the lecturer commu |
4,414 | Mathematical Statistics Videos | Years ago the ASA video taped workshop /short courses on special topics such as time series and survival analysis and categorical data analysis. They were available for chapters to rent. You might check to see what they have. Short courses at the jSM were occasionally video taped. I don't know if general math stat ... | Mathematical Statistics Videos | Years ago the ASA video taped workshop /short courses on special topics such as time series and survival analysis and categorical data analysis. They were available for chapters to rent. You might c | Mathematical Statistics Videos
Years ago the ASA video taped workshop /short courses on special topics such as time series and survival analysis and categorical data analysis. They were available for chapters to rent. You might check to see what they have. Short courses at the jSM were occasionally video taped. I d... | Mathematical Statistics Videos
Years ago the ASA video taped workshop /short courses on special topics such as time series and survival analysis and categorical data analysis. They were available for chapters to rent. You might c |
4,415 | Mathematical Statistics Videos | Bookmark http://www.edxonline.org, it's bound to have all the math videos you could wish for. I believe they are hoping to launch this fall. | Mathematical Statistics Videos | Bookmark http://www.edxonline.org, it's bound to have all the math videos you could wish for. I believe they are hoping to launch this fall. | Mathematical Statistics Videos
Bookmark http://www.edxonline.org, it's bound to have all the math videos you could wish for. I believe they are hoping to launch this fall. | Mathematical Statistics Videos
Bookmark http://www.edxonline.org, it's bound to have all the math videos you could wish for. I believe they are hoping to launch this fall. |
4,416 | Mathematical Statistics Videos | Opinionated Lessons in Statistics
http://wiki.opinionatedlessons.org/coursewiki/index.php/OpinionatedLessons.org/
Around 50 videos on statistics by Professor William H. Press of the University of Texas at Austin. Each video is around 10 to 30 minutes long.
A number of more advanced topics are coverd such as mixture mod... | Mathematical Statistics Videos | Opinionated Lessons in Statistics
http://wiki.opinionatedlessons.org/coursewiki/index.php/OpinionatedLessons.org/
Around 50 videos on statistics by Professor William H. Press of the University of Texa | Mathematical Statistics Videos
Opinionated Lessons in Statistics
http://wiki.opinionatedlessons.org/coursewiki/index.php/OpinionatedLessons.org/
Around 50 videos on statistics by Professor William H. Press of the University of Texas at Austin. Each video is around 10 to 30 minutes long.
A number of more advanced topics... | Mathematical Statistics Videos
Opinionated Lessons in Statistics
http://wiki.opinionatedlessons.org/coursewiki/index.php/OpinionatedLessons.org/
Around 50 videos on statistics by Professor William H. Press of the University of Texa |
4,417 | Mathematical Statistics Videos | Biostatistical bootcamp is a Coursera course on mathematical statistics. The videos are also available on Brian Caffo's YouTube Channel. | Mathematical Statistics Videos | Biostatistical bootcamp is a Coursera course on mathematical statistics. The videos are also available on Brian Caffo's YouTube Channel. | Mathematical Statistics Videos
Biostatistical bootcamp is a Coursera course on mathematical statistics. The videos are also available on Brian Caffo's YouTube Channel. | Mathematical Statistics Videos
Biostatistical bootcamp is a Coursera course on mathematical statistics. The videos are also available on Brian Caffo's YouTube Channel. |
4,418 | Why would R return NA as a lm() coefficient? | NA as a coefficient in a regression indicates that the variable in question is linearly related to the other variables. In your case, this means that $Q3 = a \times Q1 + b \times Q2 + c$ for some $a, b, c$. If this is the case, then there's no unique solution to the regression without dropping one of the variables. Add... | Why would R return NA as a lm() coefficient? | NA as a coefficient in a regression indicates that the variable in question is linearly related to the other variables. In your case, this means that $Q3 = a \times Q1 + b \times Q2 + c$ for some $a, | Why would R return NA as a lm() coefficient?
NA as a coefficient in a regression indicates that the variable in question is linearly related to the other variables. In your case, this means that $Q3 = a \times Q1 + b \times Q2 + c$ for some $a, b, c$. If this is the case, then there's no unique solution to the regressi... | Why would R return NA as a lm() coefficient?
NA as a coefficient in a regression indicates that the variable in question is linearly related to the other variables. In your case, this means that $Q3 = a \times Q1 + b \times Q2 + c$ for some $a, |
4,419 | Why would R return NA as a lm() coefficient? | I found this behavior when attempting to fit observations vs time, where time was given as POSIXct. lm and lsfit() both determined that the x's were co-linear. The problem was solved by subtracting the mean of the time datum to do the fit.
This appears to be a deficiency in the underlying code -- there must be some sin... | Why would R return NA as a lm() coefficient? | I found this behavior when attempting to fit observations vs time, where time was given as POSIXct. lm and lsfit() both determined that the x's were co-linear. The problem was solved by subtracting th | Why would R return NA as a lm() coefficient?
I found this behavior when attempting to fit observations vs time, where time was given as POSIXct. lm and lsfit() both determined that the x's were co-linear. The problem was solved by subtracting the mean of the time datum to do the fit.
This appears to be a deficiency in ... | Why would R return NA as a lm() coefficient?
I found this behavior when attempting to fit observations vs time, where time was given as POSIXct. lm and lsfit() both determined that the x's were co-linear. The problem was solved by subtracting th |
4,420 | Why would R return NA as a lm() coefficient? | I also got this behavior in R version 4.2.0 with an integer64 dataframe column.
It was being fetched by an SQL query via RPostgres, from a PostgreSQL column of type int8 (that's 8-byte/64-bit integer).
Luckily, the data didn't actually exceed the 2-billion 32-bit cap; so a simple downconversion helped:
df$some_field <-... | Why would R return NA as a lm() coefficient? | I also got this behavior in R version 4.2.0 with an integer64 dataframe column.
It was being fetched by an SQL query via RPostgres, from a PostgreSQL column of type int8 (that's 8-byte/64-bit integer) | Why would R return NA as a lm() coefficient?
I also got this behavior in R version 4.2.0 with an integer64 dataframe column.
It was being fetched by an SQL query via RPostgres, from a PostgreSQL column of type int8 (that's 8-byte/64-bit integer).
Luckily, the data didn't actually exceed the 2-billion 32-bit cap; so a s... | Why would R return NA as a lm() coefficient?
I also got this behavior in R version 4.2.0 with an integer64 dataframe column.
It was being fetched by an SQL query via RPostgres, from a PostgreSQL column of type int8 (that's 8-byte/64-bit integer) |
4,421 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | Could you group the data set into much smaller data sets (say 100 or 1000 or 10,000 data points) If you then calculated the median of each of the groups. If you did this with enough data sets you could plot something like the average of the results of each of the smaller sets and this woul, by running enough smaller da... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | Could you group the data set into much smaller data sets (say 100 or 1000 or 10,000 data points) If you then calculated the median of each of the groups. If you did this with enough data sets you coul | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
Could you group the data set into much smaller data sets (say 100 or 1000 or 10,000 data points) If you then calculated the median of each of the groups. If you did this with enough data sets you could plot something like the a... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
Could you group the data set into much smaller data sets (say 100 or 1000 or 10,000 data points) If you then calculated the median of each of the groups. If you did this with enough data sets you coul |
4,422 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | How about something like a binning procedure? Assume (for illustration purposes) that you know that the values are between 1 and 1 million. Set up N bins, of size S. So if S=10000, you'd have 100 bins, corresponding to values [1:10000, 10001:20000, ... , 990001:1000000]
Then, step through the values. Instead of stor... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | How about something like a binning procedure? Assume (for illustration purposes) that you know that the values are between 1 and 1 million. Set up N bins, of size S. So if S=10000, you'd have 100 bi | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
How about something like a binning procedure? Assume (for illustration purposes) that you know that the values are between 1 and 1 million. Set up N bins, of size S. So if S=10000, you'd have 100 bins, corresponding to values... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
How about something like a binning procedure? Assume (for illustration purposes) that you know that the values are between 1 and 1 million. Set up N bins, of size S. So if S=10000, you'd have 100 bi |
4,423 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | I re-direct you to my answer to a similar question. In a nutshell, it's a read once, 'on the fly' algorithm with $O(n)$ worst case complexity to compute the (exact) median. | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | I re-direct you to my answer to a similar question. In a nutshell, it's a read once, 'on the fly' algorithm with $O(n)$ worst case complexity to compute the (exact) median. | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
I re-direct you to my answer to a similar question. In a nutshell, it's a read once, 'on the fly' algorithm with $O(n)$ worst case complexity to compute the (exact) median. | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
I re-direct you to my answer to a similar question. In a nutshell, it's a read once, 'on the fly' algorithm with $O(n)$ worst case complexity to compute the (exact) median. |
4,424 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | I implemented the P-Square Algorithm for Dynamic Calculation of Quantiles and Histograms without Storing Observations in a neat Python module I wrote called LiveStats. It should solve your problem quite effectively. | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | I implemented the P-Square Algorithm for Dynamic Calculation of Quantiles and Histograms without Storing Observations in a neat Python module I wrote called LiveStats. It should solve your problem qui | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
I implemented the P-Square Algorithm for Dynamic Calculation of Quantiles and Histograms without Storing Observations in a neat Python module I wrote called LiveStats. It should solve your problem quite effectively. | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
I implemented the P-Square Algorithm for Dynamic Calculation of Quantiles and Histograms without Storing Observations in a neat Python module I wrote called LiveStats. It should solve your problem qui |
4,425 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | The Rivest-Tarjan-Selection algorithm (sometimes also called the median-of-medians algorithm) will let you compute the median element in linear-time without any sorting. For large data sets this is can be quite a bit faster than log-linear sorting. However, it won't solve your memory storage problem. | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | The Rivest-Tarjan-Selection algorithm (sometimes also called the median-of-medians algorithm) will let you compute the median element in linear-time without any sorting. For large data sets this is ca | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
The Rivest-Tarjan-Selection algorithm (sometimes also called the median-of-medians algorithm) will let you compute the median element in linear-time without any sorting. For large data sets this is can be quite a bit faster tha... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
The Rivest-Tarjan-Selection algorithm (sometimes also called the median-of-medians algorithm) will let you compute the median element in linear-time without any sorting. For large data sets this is ca |
4,426 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | I've never had to do this, so this is just a suggestion.
I see two (other) possibilities.
Half data
Load in half the data and sort
Next read in the remaining values and compare against the your sorted list.
If the new value is larger, discard it.
else put the value in the sorted list and removing the largest value ... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | I've never had to do this, so this is just a suggestion.
I see two (other) possibilities.
Half data
Load in half the data and sort
Next read in the remaining values and compare against the your sort | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
I've never had to do this, so this is just a suggestion.
I see two (other) possibilities.
Half data
Load in half the data and sort
Next read in the remaining values and compare against the your sorted list.
If the new value... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
I've never had to do this, so this is just a suggestion.
I see two (other) possibilities.
Half data
Load in half the data and sort
Next read in the remaining values and compare against the your sort |
4,427 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | The Remedian Algorithm (PDF) gives a one-pass median estimate with low storage requirements and well defined accuracy.
The remedian with base b proceeds by computing medians of groups of b observations, and then medians of these medians, until only a single estimate remains. This method merely needs k arrays of size ... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | The Remedian Algorithm (PDF) gives a one-pass median estimate with low storage requirements and well defined accuracy.
The remedian with base b proceeds by computing medians of groups of b observatio | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
The Remedian Algorithm (PDF) gives a one-pass median estimate with low storage requirements and well defined accuracy.
The remedian with base b proceeds by computing medians of groups of b observations, and then medians of the... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
The Remedian Algorithm (PDF) gives a one-pass median estimate with low storage requirements and well defined accuracy.
The remedian with base b proceeds by computing medians of groups of b observatio |
4,428 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | If the values you are using are within a certain range, say 1 to 100000, you can efficiently compute the median on an extremely large number of values (say, trillions of entries), with an integer bucket (this code taken from BSD licensed ea-utils/sam-stats.cpp)
class ibucket {
public:
int tot;
vector<int> dat;
... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | If the values you are using are within a certain range, say 1 to 100000, you can efficiently compute the median on an extremely large number of values (say, trillions of entries), with an integer buck | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
If the values you are using are within a certain range, say 1 to 100000, you can efficiently compute the median on an extremely large number of values (say, trillions of entries), with an integer bucket (this code taken from BS... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
If the values you are using are within a certain range, say 1 to 100000, you can efficiently compute the median on an extremely large number of values (say, trillions of entries), with an integer buck |
4,429 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | Another thought is in the line of random sampling. I had similar problem. My problem is that I have > 100 million data (each has 10 million). The computation took too long. You can just random sample N data points from the 10 million, then find the median on those. | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | Another thought is in the line of random sampling. I had similar problem. My problem is that I have > 100 million data (each has 10 million). The computation took too long. You can just random sampl | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
Another thought is in the line of random sampling. I had similar problem. My problem is that I have > 100 million data (each has 10 million). The computation took too long. You can just random sample N data points from the 10... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
Another thought is in the line of random sampling. I had similar problem. My problem is that I have > 100 million data (each has 10 million). The computation took too long. You can just random sampl |
4,430 | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Here's an answer to the question asked on stackoverflo... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate] | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | What is a good algorithm for estimating the median of a huge read-once data set? [duplicate]
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
4,431 | How to determine whether or not the y-axis of a graph should start at zero? | Don't use space in a graph in any way that doesn't help understanding. Space is needed to show the data!
Use your scientific (engineering, medical, social, business, ...) judgement as well as your statistical judgement. (If you are not the client or customer, talk to someone in the field to get an idea of what is inte... | How to determine whether or not the y-axis of a graph should start at zero? | Don't use space in a graph in any way that doesn't help understanding. Space is needed to show the data!
Use your scientific (engineering, medical, social, business, ...) judgement as well as your st | How to determine whether or not the y-axis of a graph should start at zero?
Don't use space in a graph in any way that doesn't help understanding. Space is needed to show the data!
Use your scientific (engineering, medical, social, business, ...) judgement as well as your statistical judgement. (If you are not the cli... | How to determine whether or not the y-axis of a graph should start at zero?
Don't use space in a graph in any way that doesn't help understanding. Space is needed to show the data!
Use your scientific (engineering, medical, social, business, ...) judgement as well as your st |
4,432 | Prediction interval for lmer() mixed effects model in R | This question and excellent exchange was the impetus for creating the predictInterval function in the merTools package. bootMer is the way to go, but for some problems it is not feasible computationally to generate bootstrapped refits of the whole model (in cases where the model is large).
In those cases, predictInter... | Prediction interval for lmer() mixed effects model in R | This question and excellent exchange was the impetus for creating the predictInterval function in the merTools package. bootMer is the way to go, but for some problems it is not feasible computational | Prediction interval for lmer() mixed effects model in R
This question and excellent exchange was the impetus for creating the predictInterval function in the merTools package. bootMer is the way to go, but for some problems it is not feasible computationally to generate bootstrapped refits of the whole model (in cases ... | Prediction interval for lmer() mixed effects model in R
This question and excellent exchange was the impetus for creating the predictInterval function in the merTools package. bootMer is the way to go, but for some problems it is not feasible computational |
4,433 | Prediction interval for lmer() mixed effects model in R | Do this by making bootMer generate a set of predictions for each parametric bootstrap replicate:
predFun <- function(fit) {
predict(fit,newDat)
}
bb <- bootMer(lme1,nsim=200,FUN=predFun,seed=101)
The output of bootMer is in a not-terribly-transparent "boot" object, but we can get the raw predictions out of the $t ... | Prediction interval for lmer() mixed effects model in R | Do this by making bootMer generate a set of predictions for each parametric bootstrap replicate:
predFun <- function(fit) {
predict(fit,newDat)
}
bb <- bootMer(lme1,nsim=200,FUN=predFun,seed=101)
| Prediction interval for lmer() mixed effects model in R
Do this by making bootMer generate a set of predictions for each parametric bootstrap replicate:
predFun <- function(fit) {
predict(fit,newDat)
}
bb <- bootMer(lme1,nsim=200,FUN=predFun,seed=101)
The output of bootMer is in a not-terribly-transparent "boot" o... | Prediction interval for lmer() mixed effects model in R
Do this by making bootMer generate a set of predictions for each parametric bootstrap replicate:
predFun <- function(fit) {
predict(fit,newDat)
}
bb <- bootMer(lme1,nsim=200,FUN=predFun,seed=101)
|
4,434 | Kullback–Leibler vs Kolmogorov-Smirnov distance | The KL-divergence is typically used in information-theoretic settings, or even Bayesian settings, to measure the information change between distributions before and after applying some inference, for example. It's not a distance in the typical (metric) sense, because of lack of symmetry and triangle inequality, and so ... | Kullback–Leibler vs Kolmogorov-Smirnov distance | The KL-divergence is typically used in information-theoretic settings, or even Bayesian settings, to measure the information change between distributions before and after applying some inference, for | Kullback–Leibler vs Kolmogorov-Smirnov distance
The KL-divergence is typically used in information-theoretic settings, or even Bayesian settings, to measure the information change between distributions before and after applying some inference, for example. It's not a distance in the typical (metric) sense, because of l... | Kullback–Leibler vs Kolmogorov-Smirnov distance
The KL-divergence is typically used in information-theoretic settings, or even Bayesian settings, to measure the information change between distributions before and after applying some inference, for |
4,435 | Kullback–Leibler vs Kolmogorov-Smirnov distance | Another way of stating the same thing as the previous answer in more layman terms:
KL Divergence - Actually provides a measure of how big of a difference are two distributions from each other. As mentioned by the previous answer, this measure isnt an appropriate distance metric since its not symmetrical. I.e. distance ... | Kullback–Leibler vs Kolmogorov-Smirnov distance | Another way of stating the same thing as the previous answer in more layman terms:
KL Divergence - Actually provides a measure of how big of a difference are two distributions from each other. As ment | Kullback–Leibler vs Kolmogorov-Smirnov distance
Another way of stating the same thing as the previous answer in more layman terms:
KL Divergence - Actually provides a measure of how big of a difference are two distributions from each other. As mentioned by the previous answer, this measure isnt an appropriate distance ... | Kullback–Leibler vs Kolmogorov-Smirnov distance
Another way of stating the same thing as the previous answer in more layman terms:
KL Divergence - Actually provides a measure of how big of a difference are two distributions from each other. As ment |
4,436 | Kullback–Leibler vs Kolmogorov-Smirnov distance | KL divergence upper bounds Kolmogrov Distance and Total variation, meaning that if two distributions $\mathcal{D}_1, \mathcal{D}_2$ have a small KL divergence, then it follows that $\mathcal{D}_1, \mathcal{D}_2$ have a small total variation and subsequently a small Kolmogrov distance (in that order).
Also check out thi... | Kullback–Leibler vs Kolmogorov-Smirnov distance | KL divergence upper bounds Kolmogrov Distance and Total variation, meaning that if two distributions $\mathcal{D}_1, \mathcal{D}_2$ have a small KL divergence, then it follows that $\mathcal{D}_1, \ma | Kullback–Leibler vs Kolmogorov-Smirnov distance
KL divergence upper bounds Kolmogrov Distance and Total variation, meaning that if two distributions $\mathcal{D}_1, \mathcal{D}_2$ have a small KL divergence, then it follows that $\mathcal{D}_1, \mathcal{D}_2$ have a small total variation and subsequently a small Kolmog... | Kullback–Leibler vs Kolmogorov-Smirnov distance
KL divergence upper bounds Kolmogrov Distance and Total variation, meaning that if two distributions $\mathcal{D}_1, \mathcal{D}_2$ have a small KL divergence, then it follows that $\mathcal{D}_1, \ma |
4,437 | Kullback–Leibler vs Kolmogorov-Smirnov distance | KS test and KL divergence test both are used to find the difference between two distributions
KS test is statistical-based and KL divergence is information theory-based
But the one major diff between KL and KS test, and why KL is more popular in machine learning is because the formulation for KL divergence is different... | Kullback–Leibler vs Kolmogorov-Smirnov distance | KS test and KL divergence test both are used to find the difference between two distributions
KS test is statistical-based and KL divergence is information theory-based
But the one major diff between | Kullback–Leibler vs Kolmogorov-Smirnov distance
KS test and KL divergence test both are used to find the difference between two distributions
KS test is statistical-based and KL divergence is information theory-based
But the one major diff between KL and KS test, and why KL is more popular in machine learning is becaus... | Kullback–Leibler vs Kolmogorov-Smirnov distance
KS test and KL divergence test both are used to find the difference between two distributions
KS test is statistical-based and KL divergence is information theory-based
But the one major diff between |
4,438 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | First of all, my first intuitive thought was: "S2 can only be the same as S1 if the traffic death rate stays constant, possibly over decades" - which certainly wouldn't have been a good assumption in the last so many decades. This already hints that one difficulty lies with implicit/unspoken temporal assumptions.
I'd ... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | First of all, my first intuitive thought was: "S2 can only be the same as S1 if the traffic death rate stays constant, possibly over decades" - which certainly wouldn't have been a good assumption in | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
First of all, my first intuitive thought was: "S2 can only be the same as S1 if the traffic death rate stays constant, possibly over decades" - which certainly wouldn't have been a good assumptio... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
First of all, my first intuitive thought was: "S2 can only be the same as S1 if the traffic death rate stays constant, possibly over decades" - which certainly wouldn't have been a good assumption in |
4,439 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | To me "1 in 80 deaths..." is by far the clearer statement. The denominator in your "1 in 80" is the set of all death events and that statement makes it explicit.
There's ambiguity in the "1 in 80 people..." formulation. You really mean "1 in 80 people who dies..." but the statement can just as easily be interpreted a... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | To me "1 in 80 deaths..." is by far the clearer statement. The denominator in your "1 in 80" is the set of all death events and that statement makes it explicit.
There's ambiguity in the "1 in 80 pe | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
To me "1 in 80 deaths..." is by far the clearer statement. The denominator in your "1 in 80" is the set of all death events and that statement makes it explicit.
There's ambiguity in the "1 in 8... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
To me "1 in 80 deaths..." is by far the clearer statement. The denominator in your "1 in 80" is the set of all death events and that statement makes it explicit.
There's ambiguity in the "1 in 80 pe |
4,440 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | It depends on whether you are describing or predicting.
"1 in 80 people will die in a car accident" is a prediction. Of all the people alive today, some time within their remaining lifetime, one in 80 will die that way.
"1 in 80 deaths are caused by a car accident" is a description. Of all the people who died in a giv... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | It depends on whether you are describing or predicting.
"1 in 80 people will die in a car accident" is a prediction. Of all the people alive today, some time within their remaining lifetime, one in 8 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
It depends on whether you are describing or predicting.
"1 in 80 people will die in a car accident" is a prediction. Of all the people alive today, some time within their remaining lifetime, one ... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
It depends on whether you are describing or predicting.
"1 in 80 people will die in a car accident" is a prediction. Of all the people alive today, some time within their remaining lifetime, one in 8 |
4,441 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | The two statements are different because of sampling bias, because car accidents are more likely to occur when people are young.
Let's make this more concrete by positing an unrealistic scenario.
Consider the two statements:
One half of all deaths are caused by a car accident.
One half of all people alive today will d... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | The two statements are different because of sampling bias, because car accidents are more likely to occur when people are young.
Let's make this more concrete by positing an unrealistic scenario.
Cons | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
The two statements are different because of sampling bias, because car accidents are more likely to occur when people are young.
Let's make this more concrete by positing an unrealistic scenario.
... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
The two statements are different because of sampling bias, because car accidents are more likely to occur when people are young.
Let's make this more concrete by positing an unrealistic scenario.
Cons |
4,442 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | Is my default interpretation indeed equivalent to Statement One?
No.
Let's say we have 800 people. 400 died: 5 from a car crash, the other 395 forgot to breathe. S1 is now true: 5/400=1/80. S2 is false: 5/800!=1/80.
The problem is that technically S2 is ambiguous because it doesn't specify how many deaths there were i... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | Is my default interpretation indeed equivalent to Statement One?
No.
Let's say we have 800 people. 400 died: 5 from a car crash, the other 395 forgot to breathe. S1 is now true: 5/400=1/80. S2 is fal | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
Is my default interpretation indeed equivalent to Statement One?
No.
Let's say we have 800 people. 400 died: 5 from a car crash, the other 395 forgot to breathe. S1 is now true: 5/400=1/80. S2 is... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
Is my default interpretation indeed equivalent to Statement One?
No.
Let's say we have 800 people. 400 died: 5 from a car crash, the other 395 forgot to breathe. S1 is now true: 5/400=1/80. S2 is fal |
4,443 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | I would agree that your interpretation of the second statement is consistent with the first statement. I would also agree that it's a perfectly reasonable interpretation of the second statement. That being said, the second statement is much more ambiguous.
The second statement can also be interpreted as:
Given a sampl... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | I would agree that your interpretation of the second statement is consistent with the first statement. I would also agree that it's a perfectly reasonable interpretation of the second statement. That | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
I would agree that your interpretation of the second statement is consistent with the first statement. I would also agree that it's a perfectly reasonable interpretation of the second statement. T... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
I would agree that your interpretation of the second statement is consistent with the first statement. I would also agree that it's a perfectly reasonable interpretation of the second statement. That |
4,444 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | The basic difference is that the two statements refer to different populations of humans, and different time frames.
"One in 80 deaths is caused by a car accident" presumably refers to the proportion of deaths in some fairly limited time period (say one year). Since the proportion of the total population using cars, an... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | The basic difference is that the two statements refer to different populations of humans, and different time frames.
"One in 80 deaths is caused by a car accident" presumably refers to the proportion | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
The basic difference is that the two statements refer to different populations of humans, and different time frames.
"One in 80 deaths is caused by a car accident" presumably refers to the proport... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
The basic difference is that the two statements refer to different populations of humans, and different time frames.
"One in 80 deaths is caused by a car accident" presumably refers to the proportion |
4,445 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | A1) Assuming everyone dies, and assuming the context of a sufficiently small period of time around that which the measurements were taken, yes, your interpretation of S2 matches S1.
A2) Yes, your interpretation of S2 is reckless. S2 can be interpreted as "1 in 80 people involved in car accidents die" which is obviously... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | A1) Assuming everyone dies, and assuming the context of a sufficiently small period of time around that which the measurements were taken, yes, your interpretation of S2 matches S1.
A2) Yes, your inte | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
A1) Assuming everyone dies, and assuming the context of a sufficiently small period of time around that which the measurements were taken, yes, your interpretation of S2 matches S1.
A2) Yes, your ... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
A1) Assuming everyone dies, and assuming the context of a sufficiently small period of time around that which the measurements were taken, yes, your interpretation of S2 matches S1.
A2) Yes, your inte |
4,446 | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?" | Yes, it is wrong, and neither phrasing seems sufficient to consistently convey your desired meaning
Speaking as a layperson, if your target is laypeople, I would definitely recommend posting over at https://english.stackexchange.com/, rather than here - your question took me a few reads to unentangle what S1 & S2 intui... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res | Yes, it is wrong, and neither phrasing seems sufficient to consistently convey your desired meaning
Speaking as a layperson, if your target is laypeople, I would definitely recommend posting over at h | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a result of a car accident?"
Yes, it is wrong, and neither phrasing seems sufficient to consistently convey your desired meaning
Speaking as a layperson, if your target is laypeople, I would definitely recommend posting over ... | Is it wrong to rephrase "1 in 80 deaths is caused by a car accident" as "1 in 80 people die as a res
Yes, it is wrong, and neither phrasing seems sufficient to consistently convey your desired meaning
Speaking as a layperson, if your target is laypeople, I would definitely recommend posting over at h |
4,447 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | There are 400 possibilities and 20 of them, each occuring with probability $\frac{1}{400}$, have the guess equal to the outcome. So the total probability of having the guess equal to the outcome is $20\cdot \frac{1}{400} = \frac{20}{400} = \frac{1}{20}$
$$\small{ \begin{array}{rc}
& \text{OUTCOME}\\
\begin{array}{}
\re... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | There are 400 possibilities and 20 of them, each occuring with probability $\frac{1}{400}$, have the guess equal to the outcome. So the total probability of having the guess equal to the outcome is $2 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
There are 400 possibilities and 20 of them, each occuring with probability $\frac{1}{400}$, have the guess equal to the outcome. So the total probability of having the guess equal to the outcome is $20\cdot \frac{1}{400} = \f... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
There are 400 possibilities and 20 of them, each occuring with probability $\frac{1}{400}$, have the guess equal to the outcome. So the total probability of having the guess equal to the outcome is $2 |
4,448 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Let's simulate it!
set.seed(2021)
R <- 10000
d <- 20
guess <- sample(seq(1, d, 1), R, replace = T)
roll <- sample(seq(1, d, 1), R, replace = T)
length(which(guess == roll))/R
I get that about $1/20$ $(486/10000)$ times my guess match the roll. If you chop off the last digit of the seed and run the code with set.seed(2... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Let's simulate it!
set.seed(2021)
R <- 10000
d <- 20
guess <- sample(seq(1, d, 1), R, replace = T)
roll <- sample(seq(1, d, 1), R, replace = T)
length(which(guess == roll))/R
I get that about $1/20$ | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Let's simulate it!
set.seed(2021)
R <- 10000
d <- 20
guess <- sample(seq(1, d, 1), R, replace = T)
roll <- sample(seq(1, d, 1), R, replace = T)
length(which(guess == roll))/R
I get that about $1/20$ $(486/10000)$ times my gu... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Let's simulate it!
set.seed(2021)
R <- 10000
d <- 20
guess <- sample(seq(1, d, 1), R, replace = T)
roll <- sample(seq(1, d, 1), R, replace = T)
length(which(guess == roll))/R
I get that about $1/20$ |
4,449 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | I don't think any of the existing answers explicitly state why the answer is 1 in 20 even if the friend is not equally likely to guess all 20 numbers (they aren't - humans are not good random number generators, and the friend might not even be trying to guess randomly).
For each possible roll $i$, let $p_i$ be the prob... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | I don't think any of the existing answers explicitly state why the answer is 1 in 20 even if the friend is not equally likely to guess all 20 numbers (they aren't - humans are not good random number g | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
I don't think any of the existing answers explicitly state why the answer is 1 in 20 even if the friend is not equally likely to guess all 20 numbers (they aren't - humans are not good random number generators, and the friend... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
I don't think any of the existing answers explicitly state why the answer is 1 in 20 even if the friend is not equally likely to guess all 20 numbers (they aren't - humans are not good random number g |
4,450 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Your friend is confusing the situation where both players roll the same specific number (which would give you 1/400) vs the situation you are in where they have to roll the same number but it could be any number (1/20). I suppose your confusion is that you have to roll the specific number your friend guessed but, which... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Your friend is confusing the situation where both players roll the same specific number (which would give you 1/400) vs the situation you are in where they have to roll the same number but it could be | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Your friend is confusing the situation where both players roll the same specific number (which would give you 1/400) vs the situation you are in where they have to roll the same number but it could be any number (1/20). I sup... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Your friend is confusing the situation where both players roll the same specific number (which would give you 1/400) vs the situation you are in where they have to roll the same number but it could be |
4,451 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Yes for any specific number $1,\ldots,20$ the probability that your friend selects that number and the dice rolls it is 1/400, but you must marginalize over all possible options:
If $X\sim \text{Unif}(1,\ldots,20)$, $Y\sim \text{Unif}(1,\ldots,20)$, Let $Z=X-Y$ then
$$
\begin{align*}
\text{P}(\text{predict roll}) &= \t... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Yes for any specific number $1,\ldots,20$ the probability that your friend selects that number and the dice rolls it is 1/400, but you must marginalize over all possible options:
If $X\sim \text{Unif} | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Yes for any specific number $1,\ldots,20$ the probability that your friend selects that number and the dice rolls it is 1/400, but you must marginalize over all possible options:
If $X\sim \text{Unif}(1,\ldots,20)$, $Y\sim \t... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Yes for any specific number $1,\ldots,20$ the probability that your friend selects that number and the dice rolls it is 1/400, but you must marginalize over all possible options:
If $X\sim \text{Unif} |
4,452 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | In more layman's terms, I would describe it like this:
Let's say the friend guesses 20--take that as a given. Now we roll the die, what's the chance that a d20 roll is 20? 1/20 = 5%.
Okay, now let's say the friend guesses 19. In this case, what's the chance that a d20 roll is 19? 1/20 = 5%.
What if the friend guesses 1... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | In more layman's terms, I would describe it like this:
Let's say the friend guesses 20--take that as a given. Now we roll the die, what's the chance that a d20 roll is 20? 1/20 = 5%.
Okay, now let's s | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
In more layman's terms, I would describe it like this:
Let's say the friend guesses 20--take that as a given. Now we roll the die, what's the chance that a d20 roll is 20? 1/20 = 5%.
Okay, now let's say the friend guesses 19.... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
In more layman's terms, I would describe it like this:
Let's say the friend guesses 20--take that as a given. Now we roll the die, what's the chance that a d20 roll is 20? 1/20 = 5%.
Okay, now let's s |
4,453 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Instead of a die roll, let's look at a coin flip. I guess whether the coin will land on heads or tails, and then you flip the coin.
There are four possible guess+outcome combinations:
I guess heads, and the coin lands on heads.
I guess heads, and the coin lands on tails.
I guess tails, and the coin lands on heads.
I g... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Instead of a die roll, let's look at a coin flip. I guess whether the coin will land on heads or tails, and then you flip the coin.
There are four possible guess+outcome combinations:
I guess heads, | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Instead of a die roll, let's look at a coin flip. I guess whether the coin will land on heads or tails, and then you flip the coin.
There are four possible guess+outcome combinations:
I guess heads, and the coin lands on hea... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Instead of a die roll, let's look at a coin flip. I guess whether the coin will land on heads or tails, and then you flip the coin.
There are four possible guess+outcome combinations:
I guess heads, |
4,454 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Your another friend is arguing that:
Guessing the roll; and
Rolling the roll.
Are independent events. But that's not what you are measuring. You a measuring:
Only the matching (of guest and roll has the same value).
There is one guest and 20 possible roll outcomes. So a chance of 1/20. | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | Your another friend is arguing that:
Guessing the roll; and
Rolling the roll.
Are independent events. But that's not what you are measuring. You a measuring:
Only the matching (of guest and roll ha | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Your another friend is arguing that:
Guessing the roll; and
Rolling the roll.
Are independent events. But that's not what you are measuring. You a measuring:
Only the matching (of guest and roll has the same value).
There... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
Your another friend is arguing that:
Guessing the roll; and
Rolling the roll.
Are independent events. But that's not what you are measuring. You a measuring:
Only the matching (of guest and roll ha |
4,455 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | The probability of your friend correctly predicting the outcome of the roll was 1 in 20.
The probability of your friend correctly predicting that that specific number would be the outcome of the roll was 1 in 400.
Let's say the friend predicted it would be a 20. If you'd have been equally impressed if he'd predicted it... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | The probability of your friend correctly predicting the outcome of the roll was 1 in 20.
The probability of your friend correctly predicting that that specific number would be the outcome of the roll | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
The probability of your friend correctly predicting the outcome of the roll was 1 in 20.
The probability of your friend correctly predicting that that specific number would be the outcome of the roll was 1 in 400.
Let's say t... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
The probability of your friend correctly predicting the outcome of the roll was 1 in 20.
The probability of your friend correctly predicting that that specific number would be the outcome of the roll |
4,456 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | What is the chance a D20 lands on 20? 5%.
What is the chance a D20 lands on 7? 5%.
What is the chance a D20 lands on [whatever number your friend named]? 5%.
No matter how you select a specific number from 1-20, a D20 has a 5% chance of matching it. Any particular number is always 5% likely, regardless of whether your ... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | What is the chance a D20 lands on 20? 5%.
What is the chance a D20 lands on 7? 5%.
What is the chance a D20 lands on [whatever number your friend named]? 5%.
No matter how you select a specific number | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
What is the chance a D20 lands on 20? 5%.
What is the chance a D20 lands on 7? 5%.
What is the chance a D20 lands on [whatever number your friend named]? 5%.
No matter how you select a specific number from 1-20, a D20 has a 5... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
What is the chance a D20 lands on 20? 5%.
What is the chance a D20 lands on 7? 5%.
What is the chance a D20 lands on [whatever number your friend named]? 5%.
No matter how you select a specific number |
4,457 | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | the probability of him randomly guessing a number and then rolling it
were both 1 in 20 so the compound probability is 1 in 400.
The probability of your friend guessing a number is not 1/20, the probability is 1. Unless there is a chance he would fail to guess anything or a chance he would guess something not on the D... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens? | the probability of him randomly guessing a number and then rolling it
were both 1 in 20 so the compound probability is 1 in 400.
The probability of your friend guessing a number is not 1/20, the prob | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
the probability of him randomly guessing a number and then rolling it
were both 1 in 20 so the compound probability is 1 in 400.
The probability of your friend guessing a number is not 1/20, the probability is 1. Unless ther... | Is there a 1 in 20 or 1 in 400 chance of guessing the outcome of a d20 roll before it happens?
the probability of him randomly guessing a number and then rolling it
were both 1 in 20 so the compound probability is 1 in 400.
The probability of your friend guessing a number is not 1/20, the prob |
4,458 | What is the difference between the forward-backward and Viterbi algorithms? | A bit of background first maybe it clears things up a bit.
When talking about HMMs (Hidden Markov Models) there are generally 3 problems to be considered:
Evaluation problem
Evaluation problem answers the question: what is the probability that a particular sequence of symbols is produced by a particular model?
For e... | What is the difference between the forward-backward and Viterbi algorithms? | A bit of background first maybe it clears things up a bit.
When talking about HMMs (Hidden Markov Models) there are generally 3 problems to be considered:
Evaluation problem
Evaluation problem answe | What is the difference between the forward-backward and Viterbi algorithms?
A bit of background first maybe it clears things up a bit.
When talking about HMMs (Hidden Markov Models) there are generally 3 problems to be considered:
Evaluation problem
Evaluation problem answers the question: what is the probability tha... | What is the difference between the forward-backward and Viterbi algorithms?
A bit of background first maybe it clears things up a bit.
When talking about HMMs (Hidden Markov Models) there are generally 3 problems to be considered:
Evaluation problem
Evaluation problem answe |
4,459 | What is the difference between the forward-backward and Viterbi algorithms? | Forward-Backward gives marginal probability for each individual state, Viterbi gives probability of the most likely sequence of states. For instance if your HMM task is to predict sunny vs. rainy weather for each day, Forward Backward would tell you the probability of it being "sunny" for each day, Viterbi would give t... | What is the difference between the forward-backward and Viterbi algorithms? | Forward-Backward gives marginal probability for each individual state, Viterbi gives probability of the most likely sequence of states. For instance if your HMM task is to predict sunny vs. rainy weat | What is the difference between the forward-backward and Viterbi algorithms?
Forward-Backward gives marginal probability for each individual state, Viterbi gives probability of the most likely sequence of states. For instance if your HMM task is to predict sunny vs. rainy weather for each day, Forward Backward would tel... | What is the difference between the forward-backward and Viterbi algorithms?
Forward-Backward gives marginal probability for each individual state, Viterbi gives probability of the most likely sequence of states. For instance if your HMM task is to predict sunny vs. rainy weat |
4,460 | What is the difference between the forward-backward and Viterbi algorithms? | I find these two following slides from {2} to be really good to situate the forward-backward and Viterbi algorithms amongst all other typical algorithms used with HMM:
Notes:
$x$ is the observed emission(s), $\pi$ are the parameters of the HMM.
path = a sequence of emissions
decoding = inference
learning = training ... | What is the difference between the forward-backward and Viterbi algorithms? | I find these two following slides from {2} to be really good to situate the forward-backward and Viterbi algorithms amongst all other typical algorithms used with HMM:
Notes:
$x$ is the observed em | What is the difference between the forward-backward and Viterbi algorithms?
I find these two following slides from {2} to be really good to situate the forward-backward and Viterbi algorithms amongst all other typical algorithms used with HMM:
Notes:
$x$ is the observed emission(s), $\pi$ are the parameters of the H... | What is the difference between the forward-backward and Viterbi algorithms?
I find these two following slides from {2} to be really good to situate the forward-backward and Viterbi algorithms amongst all other typical algorithms used with HMM:
Notes:
$x$ is the observed em |
4,461 | What is the difference between the forward-backward and Viterbi algorithms? | Morat's answer is false on one point: Baum-Welch is an Expectation-Maximization algorithm, used to train an HMM's parameters. It uses the forward-backward algorithm during each iteration. The forward-backward algorithm really is just a combination of the forward and backward algorithms: one forward pass, one backward p... | What is the difference between the forward-backward and Viterbi algorithms? | Morat's answer is false on one point: Baum-Welch is an Expectation-Maximization algorithm, used to train an HMM's parameters. It uses the forward-backward algorithm during each iteration. The forward- | What is the difference between the forward-backward and Viterbi algorithms?
Morat's answer is false on one point: Baum-Welch is an Expectation-Maximization algorithm, used to train an HMM's parameters. It uses the forward-backward algorithm during each iteration. The forward-backward algorithm really is just a combinat... | What is the difference between the forward-backward and Viterbi algorithms?
Morat's answer is false on one point: Baum-Welch is an Expectation-Maximization algorithm, used to train an HMM's parameters. It uses the forward-backward algorithm during each iteration. The forward- |
4,462 | What is the difference between the forward-backward and Viterbi algorithms? | @Yaroslav Bulatov had a precise answer. I would add one example of it to tell the differences between forward-backward and Viterbi algorithms.
Suppose we have an this HMM (from Wikipedia HMM page). Note, the model is already given, so there is no learning from data task here.
Suppose our data is a length 4 sequence. ... | What is the difference between the forward-backward and Viterbi algorithms? | @Yaroslav Bulatov had a precise answer. I would add one example of it to tell the differences between forward-backward and Viterbi algorithms.
Suppose we have an this HMM (from Wikipedia HMM page). No | What is the difference between the forward-backward and Viterbi algorithms?
@Yaroslav Bulatov had a precise answer. I would add one example of it to tell the differences between forward-backward and Viterbi algorithms.
Suppose we have an this HMM (from Wikipedia HMM page). Note, the model is already given, so there is ... | What is the difference between the forward-backward and Viterbi algorithms?
@Yaroslav Bulatov had a precise answer. I would add one example of it to tell the differences between forward-backward and Viterbi algorithms.
Suppose we have an this HMM (from Wikipedia HMM page). No |
4,463 | Linear kernel and non-linear kernel for support vector machine? | Usually, the decision is whether to use linear or an RBF (aka Gaussian) kernel. There are two main factors to consider:
Solving the optimisation problem for a linear kernel is much faster, see e.g. LIBLINEAR.
Typically, the best possible predictive performance is better for a nonlinear kernel (or at least as good as t... | Linear kernel and non-linear kernel for support vector machine? | Usually, the decision is whether to use linear or an RBF (aka Gaussian) kernel. There are two main factors to consider:
Solving the optimisation problem for a linear kernel is much faster, see e.g. L | Linear kernel and non-linear kernel for support vector machine?
Usually, the decision is whether to use linear or an RBF (aka Gaussian) kernel. There are two main factors to consider:
Solving the optimisation problem for a linear kernel is much faster, see e.g. LIBLINEAR.
Typically, the best possible predictive perfor... | Linear kernel and non-linear kernel for support vector machine?
Usually, the decision is whether to use linear or an RBF (aka Gaussian) kernel. There are two main factors to consider:
Solving the optimisation problem for a linear kernel is much faster, see e.g. L |
4,464 | Linear kernel and non-linear kernel for support vector machine? | Andrew Ng gives a nice rule of thumb explanation in this video starting 14:46, though the whole video is worth watching.
Key Points
Use linear kernel when number of features is larger than number of observations.
Use gaussian kernel when number of observations is larger than number of features.
If number of observatio... | Linear kernel and non-linear kernel for support vector machine? | Andrew Ng gives a nice rule of thumb explanation in this video starting 14:46, though the whole video is worth watching.
Key Points
Use linear kernel when number of features is larger than number of | Linear kernel and non-linear kernel for support vector machine?
Andrew Ng gives a nice rule of thumb explanation in this video starting 14:46, though the whole video is worth watching.
Key Points
Use linear kernel when number of features is larger than number of observations.
Use gaussian kernel when number of observa... | Linear kernel and non-linear kernel for support vector machine?
Andrew Ng gives a nice rule of thumb explanation in this video starting 14:46, though the whole video is worth watching.
Key Points
Use linear kernel when number of features is larger than number of |
4,465 | Logistic Regression in R (Odds Ratio) | if you want to interpret the estimated effects as relative odds ratios, just do exp(coef(x)) (gives you $e^\beta$, the multiplicative change in the odds ratio for $y=1$ if the covariate associated with $\beta$ increases by 1). For profile likelihood intervals for this quantity, you can do
require(MASS)
exp(cbind(coef(x... | Logistic Regression in R (Odds Ratio) | if you want to interpret the estimated effects as relative odds ratios, just do exp(coef(x)) (gives you $e^\beta$, the multiplicative change in the odds ratio for $y=1$ if the covariate associated wit | Logistic Regression in R (Odds Ratio)
if you want to interpret the estimated effects as relative odds ratios, just do exp(coef(x)) (gives you $e^\beta$, the multiplicative change in the odds ratio for $y=1$ if the covariate associated with $\beta$ increases by 1). For profile likelihood intervals for this quantity, you... | Logistic Regression in R (Odds Ratio)
if you want to interpret the estimated effects as relative odds ratios, just do exp(coef(x)) (gives you $e^\beta$, the multiplicative change in the odds ratio for $y=1$ if the covariate associated wit |
4,466 | Logistic Regression in R (Odds Ratio) | You are right that R's output usually contains only essential information, and more needs to be calculated separately.
N <- 100 # generate some data
X1 <- rnorm(N, 175, 7)
X2 <- rnorm(N, 30, 8)
X3 <- abs(rnorm(N, 60, 30))
Y <- 0.5*X1 - 0.3*X2 - 0.4*X3 + 10 + rnorm(N, 0, 12)
# dichotomize Y and do logi... | Logistic Regression in R (Odds Ratio) | You are right that R's output usually contains only essential information, and more needs to be calculated separately.
N <- 100 # generate some data
X1 <- rnorm(N, 175, 7)
X2 <- rnorm(N | Logistic Regression in R (Odds Ratio)
You are right that R's output usually contains only essential information, and more needs to be calculated separately.
N <- 100 # generate some data
X1 <- rnorm(N, 175, 7)
X2 <- rnorm(N, 30, 8)
X3 <- abs(rnorm(N, 60, 30))
Y <- 0.5*X1 - 0.3*X2 - 0.4*X3 + 10 + rnorm(... | Logistic Regression in R (Odds Ratio)
You are right that R's output usually contains only essential information, and more needs to be calculated separately.
N <- 100 # generate some data
X1 <- rnorm(N, 175, 7)
X2 <- rnorm(N |
4,467 | Logistic Regression in R (Odds Ratio) | The UCLA stats page has a nice walk-through of performing logistic regression in R. It includes a brief section on calculating odds ratios. | Logistic Regression in R (Odds Ratio) | The UCLA stats page has a nice walk-through of performing logistic regression in R. It includes a brief section on calculating odds ratios. | Logistic Regression in R (Odds Ratio)
The UCLA stats page has a nice walk-through of performing logistic regression in R. It includes a brief section on calculating odds ratios. | Logistic Regression in R (Odds Ratio)
The UCLA stats page has a nice walk-through of performing logistic regression in R. It includes a brief section on calculating odds ratios. |
4,468 | Logistic Regression in R (Odds Ratio) | The epiDisplay package does this very easily.
library(epiDisplay)
data(Wells, package="carData")
glm1 <- glm(switch~arsenic+distance+education+association,
family=binomial, data=Wells)
logistic.display(glm1)
Logistic regression predicting switch : yes vs no
crude OR(95%CI) ... | Logistic Regression in R (Odds Ratio) | The epiDisplay package does this very easily.
library(epiDisplay)
data(Wells, package="carData")
glm1 <- glm(switch~arsenic+distance+education+association,
family=binomial, data=Wells)
lo | Logistic Regression in R (Odds Ratio)
The epiDisplay package does this very easily.
library(epiDisplay)
data(Wells, package="carData")
glm1 <- glm(switch~arsenic+distance+education+association,
family=binomial, data=Wells)
logistic.display(glm1)
Logistic regression predicting switch : yes vs no
... | Logistic Regression in R (Odds Ratio)
The epiDisplay package does this very easily.
library(epiDisplay)
data(Wells, package="carData")
glm1 <- glm(switch~arsenic+distance+education+association,
family=binomial, data=Wells)
lo |
4,469 | Logistic Regression in R (Odds Ratio) | I tried @fabians's answer. It gave different results compared to @lockedoff's and @Edward answer when using a binary predictor. Please be careful when choosing the method.
For my own model, using @fabian's method, it gave Odds ratio 4.01 with confidence interval [1.183976, 25.038871] while @lockedoff's answer gave odd... | Logistic Regression in R (Odds Ratio) | I tried @fabians's answer. It gave different results compared to @lockedoff's and @Edward answer when using a binary predictor. Please be careful when choosing the method.
For my own model, using @fa | Logistic Regression in R (Odds Ratio)
I tried @fabians's answer. It gave different results compared to @lockedoff's and @Edward answer when using a binary predictor. Please be careful when choosing the method.
For my own model, using @fabian's method, it gave Odds ratio 4.01 with confidence interval [1.183976, 25.0388... | Logistic Regression in R (Odds Ratio)
I tried @fabians's answer. It gave different results compared to @lockedoff's and @Edward answer when using a binary predictor. Please be careful when choosing the method.
For my own model, using @fa |
4,470 | Logistic Regression in R (Odds Ratio) | R has been mature with regard to odds ratio calculations more more than two decades. It's best to think about this in general terms. For example what if x1 and x2 are continuous and have nonlinear effects and interact with each other? Here is example code where the inter-quartile-range effect of x1 is computed, adju... | Logistic Regression in R (Odds Ratio) | R has been mature with regard to odds ratio calculations more more than two decades. It's best to think about this in general terms. For example what if x1 and x2 are continuous and have nonlinear e | Logistic Regression in R (Odds Ratio)
R has been mature with regard to odds ratio calculations more more than two decades. It's best to think about this in general terms. For example what if x1 and x2 are continuous and have nonlinear effects and interact with each other? Here is example code where the inter-quartil... | Logistic Regression in R (Odds Ratio)
R has been mature with regard to odds ratio calculations more more than two decades. It's best to think about this in general terms. For example what if x1 and x2 are continuous and have nonlinear e |
4,471 | Logistic Regression in R (Odds Ratio) | Similar to the choosen answer, but there is a direct command to get the exp(coefficients) and the intervals in one line.
Current choosen answer:
require(MASS)
exp(cbind(coef(x), confint(x)))
In one line:
summ(my_model, exp = T)
(I could not make a comment since I'm still a newbi and don't have enough reputation poi... | Logistic Regression in R (Odds Ratio) | Similar to the choosen answer, but there is a direct command to get the exp(coefficients) and the intervals in one line.
Current choosen answer:
require(MASS)
exp(cbind(coef(x), confint(x)))
In one | Logistic Regression in R (Odds Ratio)
Similar to the choosen answer, but there is a direct command to get the exp(coefficients) and the intervals in one line.
Current choosen answer:
require(MASS)
exp(cbind(coef(x), confint(x)))
In one line:
summ(my_model, exp = T)
(I could not make a comment since I'm still a newb... | Logistic Regression in R (Odds Ratio)
Similar to the choosen answer, but there is a direct command to get the exp(coefficients) and the intervals in one line.
Current choosen answer:
require(MASS)
exp(cbind(coef(x), confint(x)))
In one |
4,472 | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | Note that the Shapiro-Wilk is a powerful test of normality.
The best approach is really to have a good idea of how sensitive any procedure you want to use is to various kinds of non-normality (how badly non-normal does it have to be in that way for it to affect your inference more than you can accept).
An informal appr... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | Note that the Shapiro-Wilk is a powerful test of normality.
The best approach is really to have a good idea of how sensitive any procedure you want to use is to various kinds of non-normality (how bad | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
Note that the Shapiro-Wilk is a powerful test of normality.
The best approach is really to have a good idea of how sensitive any procedure you want to use is to various kinds of non-normality (how badly non-normal does it have to be in that w... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
Note that the Shapiro-Wilk is a powerful test of normality.
The best approach is really to have a good idea of how sensitive any procedure you want to use is to various kinds of non-normality (how bad |
4,473 | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | Without contradicting any of the excellent answers here, I have one rule of thumb which is often (but not always) decisive. (A passing comment in the answer by @Dante seems pertinent too.)
It sometimes seems too obvious to state, but here you are.
I am happy to call a distribution non-normal if I think I can offer a... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | Without contradicting any of the excellent answers here, I have one rule of thumb which is often (but not always) decisive. (A passing comment in the answer by @Dante seems pertinent too.)
It someti | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
Without contradicting any of the excellent answers here, I have one rule of thumb which is often (but not always) decisive. (A passing comment in the answer by @Dante seems pertinent too.)
It sometimes seems too obvious to state, but here y... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
Without contradicting any of the excellent answers here, I have one rule of thumb which is often (but not always) decisive. (A passing comment in the answer by @Dante seems pertinent too.)
It someti |
4,474 | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | Like @Glen_b said, you can compare your data with the data you're sure is normal - the data you generated yourself, and then rely on your gut feeling :)
The following is an example from OpenIntro Statistics textbook
Let's have a look at this Q-Q Plot:
Is it normal? Let's compare it with normally distributed data:
Thi... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | Like @Glen_b said, you can compare your data with the data you're sure is normal - the data you generated yourself, and then rely on your gut feeling :)
The following is an example from OpenIntro Stat | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
Like @Glen_b said, you can compare your data with the data you're sure is normal - the data you generated yourself, and then rely on your gut feeling :)
The following is an example from OpenIntro Statistics textbook
Let's have a look at this ... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
Like @Glen_b said, you can compare your data with the data you're sure is normal - the data you generated yourself, and then rely on your gut feeling :)
The following is an example from OpenIntro Stat |
4,475 | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | There are many tests of normality. One usually focuses on the null hypothesis, namely, "$H_0: F=Normal$". However, little attention is paid to the alternative hypothesis: "against what"?
Typically, tests that consider any other distribution as the alternative hypothesis have low power when compared against tests with t... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | There are many tests of normality. One usually focuses on the null hypothesis, namely, "$H_0: F=Normal$". However, little attention is paid to the alternative hypothesis: "against what"?
Typically, te | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
There are many tests of normality. One usually focuses on the null hypothesis, namely, "$H_0: F=Normal$". However, little attention is paid to the alternative hypothesis: "against what"?
Typically, tests that consider any other distribution a... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
There are many tests of normality. One usually focuses on the null hypothesis, namely, "$H_0: F=Normal$". However, little attention is paid to the alternative hypothesis: "against what"?
Typically, te |
4,476 | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | When teaching my regression modeling strategies course, this topic always troubles my students and me. I tell them that our graphical assessments are always subjective, and I tend to worry about the graphs more early in the day than later when I'm tired. Adding formal statistical tests doesn't help enough: tests can ... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality? | When teaching my regression modeling strategies course, this topic always troubles my students and me. I tell them that our graphical assessments are always subjective, and I tend to worry about the | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
When teaching my regression modeling strategies course, this topic always troubles my students and me. I tell them that our graphical assessments are always subjective, and I tend to worry about the graphs more early in the day than later wh... | Interpreting QQplot - Is there any rule of thumb to decide for non-normality?
When teaching my regression modeling strategies course, this topic always troubles my students and me. I tell them that our graphical assessments are always subjective, and I tend to worry about the |
4,477 | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of $Z_n$ disappear for $n \geq 3$? | We can take various approaches to this, any of which may seem intuitive to some people and less than intuitive to others. To accommodate such variation, this answer surveys several such approaches, covering the major divisions of mathematical thought--analysis (the infinite and the infinitesimal), geometry/topology (s... | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of | We can take various approaches to this, any of which may seem intuitive to some people and less than intuitive to others. To accommodate such variation, this answer surveys several such approaches, c | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of $Z_n$ disappear for $n \geq 3$?
We can take various approaches to this, any of which may seem intuitive to some people and less than intuitive to others. To accommodate such variation, this answer surveys several such a... | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of
We can take various approaches to this, any of which may seem intuitive to some people and less than intuitive to others. To accommodate such variation, this answer surveys several such approaches, c |
4,478 | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of $Z_n$ disappear for $n \geq 3$? | I think the more surprising thing is that you get the sharp peak for $n=2$.
The Central Limit Theorem says that for large enough sample sizes the distribution of the mean (and the sum is just the mean times $n$, a fixed constant for each graph) will be approximately normal. It turns out that the uniform distribution... | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of | I think the more surprising thing is that you get the sharp peak for $n=2$.
The Central Limit Theorem says that for large enough sample sizes the distribution of the mean (and the sum is just the me | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of $Z_n$ disappear for $n \geq 3$?
I think the more surprising thing is that you get the sharp peak for $n=2$.
The Central Limit Theorem says that for large enough sample sizes the distribution of the mean (and the sum is... | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of
I think the more surprising thing is that you get the sharp peak for $n=2$.
The Central Limit Theorem says that for large enough sample sizes the distribution of the mean (and the sum is just the me |
4,479 | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of $Z_n$ disappear for $n \geq 3$? | You could argue that the probability density function of a uniform random variable is finite,
so its integral the cumulative density function of a uniform random variable is continuous,
so the probability density function of the sum of two uniform random variables is continuous,
so its integral the cumulative densit... | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of | You could argue that the probability density function of a uniform random variable is finite,
so its integral the cumulative density function of a uniform random variable is continuous,
so the proba | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of $Z_n$ disappear for $n \geq 3$?
You could argue that the probability density function of a uniform random variable is finite,
so its integral the cumulative density function of a uniform random variable is continuous,
... | Consider the sum of $n$ uniform distributions on $[0,1]$, or $Z_n$. Why does the cusp in the PDF of
You could argue that the probability density function of a uniform random variable is finite,
so its integral the cumulative density function of a uniform random variable is continuous,
so the proba |
4,480 | How do R and Python complement each other in data science? | They are complementary. It is true that both can do the same things, yet this can be said of most languages. Each has its strengths and weaknesses. The common outlook seems to be that Python is best for data gathering and preparation, as well as for textual analysis. R is considered best for the data analysis, as it is... | How do R and Python complement each other in data science? | They are complementary. It is true that both can do the same things, yet this can be said of most languages. Each has its strengths and weaknesses. The common outlook seems to be that Python is best f | How do R and Python complement each other in data science?
They are complementary. It is true that both can do the same things, yet this can be said of most languages. Each has its strengths and weaknesses. The common outlook seems to be that Python is best for data gathering and preparation, as well as for textual ana... | How do R and Python complement each other in data science?
They are complementary. It is true that both can do the same things, yet this can be said of most languages. Each has its strengths and weaknesses. The common outlook seems to be that Python is best f |
4,481 | How do R and Python complement each other in data science? | I will try to formulate an answer touching the main points where the two languages come into play for data science / statistics / data analysis and the like, as someone who uses both.
The workflow in data analysis generally consists of the following steps:
Fetching the data from some sort of source (most likely a SQL/... | How do R and Python complement each other in data science? | I will try to formulate an answer touching the main points where the two languages come into play for data science / statistics / data analysis and the like, as someone who uses both.
The workflow in | How do R and Python complement each other in data science?
I will try to formulate an answer touching the main points where the two languages come into play for data science / statistics / data analysis and the like, as someone who uses both.
The workflow in data analysis generally consists of the following steps:
Fet... | How do R and Python complement each other in data science?
I will try to formulate an answer touching the main points where the two languages come into play for data science / statistics / data analysis and the like, as someone who uses both.
The workflow in |
4,482 | How do R and Python complement each other in data science? | Python is a general programming language: therefore, it is good for doing many other tasks in addition to data analysis. For example, if we want to automate our model execution in production server, then python is a really good choice. Other examples include connecting to hardware/sensors to read data, interacting with... | How do R and Python complement each other in data science? | Python is a general programming language: therefore, it is good for doing many other tasks in addition to data analysis. For example, if we want to automate our model execution in production server, t | How do R and Python complement each other in data science?
Python is a general programming language: therefore, it is good for doing many other tasks in addition to data analysis. For example, if we want to automate our model execution in production server, then python is a really good choice. Other examples include co... | How do R and Python complement each other in data science?
Python is a general programming language: therefore, it is good for doing many other tasks in addition to data analysis. For example, if we want to automate our model execution in production server, t |
4,483 | How do R and Python complement each other in data science? | Programmers of all stripes underestimate how much language choices are cultural. Web developers like Node.js. Scientists like Python. As a polyglot software engineer who can handle Javascript's fluidity and Java's rigidity all the same, I've realized there is not any intrinsic reason these languages are bad at each oth... | How do R and Python complement each other in data science? | Programmers of all stripes underestimate how much language choices are cultural. Web developers like Node.js. Scientists like Python. As a polyglot software engineer who can handle Javascript's fluidi | How do R and Python complement each other in data science?
Programmers of all stripes underestimate how much language choices are cultural. Web developers like Node.js. Scientists like Python. As a polyglot software engineer who can handle Javascript's fluidity and Java's rigidity all the same, I've realized there is n... | How do R and Python complement each other in data science?
Programmers of all stripes underestimate how much language choices are cultural. Web developers like Node.js. Scientists like Python. As a polyglot software engineer who can handle Javascript's fluidi |
4,484 | How do R and Python complement each other in data science? | I am an R user but I think Python is the future (I don't think it's the syntax)
Python is the future
The benefit of Python is as other people have already mentioned the much wider support, and, for programmers, more logical syntax.
Also the ability that you can translate findings from your analysis into a production sy... | How do R and Python complement each other in data science? | I am an R user but I think Python is the future (I don't think it's the syntax)
Python is the future
The benefit of Python is as other people have already mentioned the much wider support, and, for pr | How do R and Python complement each other in data science?
I am an R user but I think Python is the future (I don't think it's the syntax)
Python is the future
The benefit of Python is as other people have already mentioned the much wider support, and, for programmers, more logical syntax.
Also the ability that you can... | How do R and Python complement each other in data science?
I am an R user but I think Python is the future (I don't think it's the syntax)
Python is the future
The benefit of Python is as other people have already mentioned the much wider support, and, for pr |
4,485 | How do R and Python complement each other in data science? | If you look at R as more of a statistical tool and not as a programming language, it is really great. It has far more flexibility than Stata or SPSS, but can do everything they can as well. I learned Stata during college, and R was easy enough to look at because I already had the perspective of the statistical tool and... | How do R and Python complement each other in data science? | If you look at R as more of a statistical tool and not as a programming language, it is really great. It has far more flexibility than Stata or SPSS, but can do everything they can as well. I learned | How do R and Python complement each other in data science?
If you look at R as more of a statistical tool and not as a programming language, it is really great. It has far more flexibility than Stata or SPSS, but can do everything they can as well. I learned Stata during college, and R was easy enough to look at becaus... | How do R and Python complement each other in data science?
If you look at R as more of a statistical tool and not as a programming language, it is really great. It has far more flexibility than Stata or SPSS, but can do everything they can as well. I learned |
4,486 | How do R and Python complement each other in data science? | Adding to some of the prior answers:
In my experience, there's nothing easier than using R's dplyr + tidyr, ggplot and Rmarkdown in getting from raw data to presentable results. Python offers a lot, and I'm using it more and more, but I sure love the way Hadley's packages tie together. | How do R and Python complement each other in data science? | Adding to some of the prior answers:
In my experience, there's nothing easier than using R's dplyr + tidyr, ggplot and Rmarkdown in getting from raw data to presentable results. Python offers a lot, | How do R and Python complement each other in data science?
Adding to some of the prior answers:
In my experience, there's nothing easier than using R's dplyr + tidyr, ggplot and Rmarkdown in getting from raw data to presentable results. Python offers a lot, and I'm using it more and more, but I sure love the way Hadle... | How do R and Python complement each other in data science?
Adding to some of the prior answers:
In my experience, there's nothing easier than using R's dplyr + tidyr, ggplot and Rmarkdown in getting from raw data to presentable results. Python offers a lot, |
4,487 | How do R and Python complement each other in data science? | Python has a wide adoption outside science, so you benefit from all that. As "An Angry Guide to R" points out, R was developed by a community, which had to the first order zero software developers.
I would say that today R has two main strengths: some really mature highly specialized packages in some areas, and state-... | How do R and Python complement each other in data science? | Python has a wide adoption outside science, so you benefit from all that. As "An Angry Guide to R" points out, R was developed by a community, which had to the first order zero software developers.
I | How do R and Python complement each other in data science?
Python has a wide adoption outside science, so you benefit from all that. As "An Angry Guide to R" points out, R was developed by a community, which had to the first order zero software developers.
I would say that today R has two main strengths: some really m... | How do R and Python complement each other in data science?
Python has a wide adoption outside science, so you benefit from all that. As "An Angry Guide to R" points out, R was developed by a community, which had to the first order zero software developers.
I |
4,488 | How do R and Python complement each other in data science? | As described in other answers, Python is a good general-purpose programming language, whereas R has serious flaws as a programming language but has a richer set of data-analysis libraries. In recent years, Python has been catching up to R with the development of mature data-analysis libraries such as scikit-learn, wher... | How do R and Python complement each other in data science? | As described in other answers, Python is a good general-purpose programming language, whereas R has serious flaws as a programming language but has a richer set of data-analysis libraries. In recent y | How do R and Python complement each other in data science?
As described in other answers, Python is a good general-purpose programming language, whereas R has serious flaws as a programming language but has a richer set of data-analysis libraries. In recent years, Python has been catching up to R with the development o... | How do R and Python complement each other in data science?
As described in other answers, Python is a good general-purpose programming language, whereas R has serious flaws as a programming language but has a richer set of data-analysis libraries. In recent y |
4,489 | How to determine best cutoff point and its confidence interval using ROC curve in R? | Thanks to all who aswered this question. I agree that there could be no one correct answer and criteria greatly depend on the aims that stand behind of the certain diagnostic test.
Finally I had found an R package OptimalCutpoints dedicated exactly to finding cutoff point in such type of analysis. Actually there are se... | How to determine best cutoff point and its confidence interval using ROC curve in R? | Thanks to all who aswered this question. I agree that there could be no one correct answer and criteria greatly depend on the aims that stand behind of the certain diagnostic test.
Finally I had found | How to determine best cutoff point and its confidence interval using ROC curve in R?
Thanks to all who aswered this question. I agree that there could be no one correct answer and criteria greatly depend on the aims that stand behind of the certain diagnostic test.
Finally I had found an R package OptimalCutpoints dedi... | How to determine best cutoff point and its confidence interval using ROC curve in R?
Thanks to all who aswered this question. I agree that there could be no one correct answer and criteria greatly depend on the aims that stand behind of the certain diagnostic test.
Finally I had found |
4,490 | How to determine best cutoff point and its confidence interval using ROC curve in R? | In my opinion, there are multiple cut-off options. You might weight sensitivity and specificity differently (for example, maybe for you it is more important to have a high sensitive test even though this means having a low specific test. Or vice-versa).
If sensitivity and specificity have the same importance to you, on... | How to determine best cutoff point and its confidence interval using ROC curve in R? | In my opinion, there are multiple cut-off options. You might weight sensitivity and specificity differently (for example, maybe for you it is more important to have a high sensitive test even though t | How to determine best cutoff point and its confidence interval using ROC curve in R?
In my opinion, there are multiple cut-off options. You might weight sensitivity and specificity differently (for example, maybe for you it is more important to have a high sensitive test even though this means having a low specific tes... | How to determine best cutoff point and its confidence interval using ROC curve in R?
In my opinion, there are multiple cut-off options. You might weight sensitivity and specificity differently (for example, maybe for you it is more important to have a high sensitive test even though t |
4,491 | How to determine best cutoff point and its confidence interval using ROC curve in R? | Resist the temptation to find a cutoff. Unless you have a pre-specified utility/loss/cost function, a cutoff flies in the face of optimal decision-making. And an ROC curve is irrelevant to this issue. | How to determine best cutoff point and its confidence interval using ROC curve in R? | Resist the temptation to find a cutoff. Unless you have a pre-specified utility/loss/cost function, a cutoff flies in the face of optimal decision-making. And an ROC curve is irrelevant to this issu | How to determine best cutoff point and its confidence interval using ROC curve in R?
Resist the temptation to find a cutoff. Unless you have a pre-specified utility/loss/cost function, a cutoff flies in the face of optimal decision-making. And an ROC curve is irrelevant to this issue. | How to determine best cutoff point and its confidence interval using ROC curve in R?
Resist the temptation to find a cutoff. Unless you have a pre-specified utility/loss/cost function, a cutoff flies in the face of optimal decision-making. And an ROC curve is irrelevant to this issu |
4,492 | How to determine best cutoff point and its confidence interval using ROC curve in R? | Mathematically speaking, you need another condition to solve for the cut-off.
You may translate @Andrea's point to: "use external knowledge about the underlying problem".
Example conditions:
for this application, we need sensitivity >= x, and/or specificity >= y.
a false negative is 10 x as bad as a false positive. (... | How to determine best cutoff point and its confidence interval using ROC curve in R? | Mathematically speaking, you need another condition to solve for the cut-off.
You may translate @Andrea's point to: "use external knowledge about the underlying problem".
Example conditions:
for thi | How to determine best cutoff point and its confidence interval using ROC curve in R?
Mathematically speaking, you need another condition to solve for the cut-off.
You may translate @Andrea's point to: "use external knowledge about the underlying problem".
Example conditions:
for this application, we need sensitivity ... | How to determine best cutoff point and its confidence interval using ROC curve in R?
Mathematically speaking, you need another condition to solve for the cut-off.
You may translate @Andrea's point to: "use external knowledge about the underlying problem".
Example conditions:
for thi |
4,493 | How to determine best cutoff point and its confidence interval using ROC curve in R? | Visualize accuracy versus cutoff. You can read more details at ROCR documentation and very nice presentation from the same. | How to determine best cutoff point and its confidence interval using ROC curve in R? | Visualize accuracy versus cutoff. You can read more details at ROCR documentation and very nice presentation from the same. | How to determine best cutoff point and its confidence interval using ROC curve in R?
Visualize accuracy versus cutoff. You can read more details at ROCR documentation and very nice presentation from the same. | How to determine best cutoff point and its confidence interval using ROC curve in R?
Visualize accuracy versus cutoff. You can read more details at ROCR documentation and very nice presentation from the same. |
4,494 | How to determine best cutoff point and its confidence interval using ROC curve in R? | What's more important - there's very few datapoints behind this curve. When you do decide how you're going to make the sensitivity/specificity tradeoff I'd strongly encourage you to bootstrap the curve and the resulting cutoff number. You may find that there's a lot of uncertainty in your estimated best cutoff. | How to determine best cutoff point and its confidence interval using ROC curve in R? | What's more important - there's very few datapoints behind this curve. When you do decide how you're going to make the sensitivity/specificity tradeoff I'd strongly encourage you to bootstrap the cur | How to determine best cutoff point and its confidence interval using ROC curve in R?
What's more important - there's very few datapoints behind this curve. When you do decide how you're going to make the sensitivity/specificity tradeoff I'd strongly encourage you to bootstrap the curve and the resulting cutoff number.... | How to determine best cutoff point and its confidence interval using ROC curve in R?
What's more important - there's very few datapoints behind this curve. When you do decide how you're going to make the sensitivity/specificity tradeoff I'd strongly encourage you to bootstrap the cur |
4,495 | Interpretation of R's output for binomial regression | What you have done is logistic regression. This can be done in basically any statistical software, and the output will be similar (at least in content, albeit the presentation may differ). There is a guide to logistic regression with R on UCLA's excellent statistics help website. If you are unfamiliar with this, my ... | Interpretation of R's output for binomial regression | What you have done is logistic regression. This can be done in basically any statistical software, and the output will be similar (at least in content, albeit the presentation may differ). There is | Interpretation of R's output for binomial regression
What you have done is logistic regression. This can be done in basically any statistical software, and the output will be similar (at least in content, albeit the presentation may differ). There is a guide to logistic regression with R on UCLA's excellent statistic... | Interpretation of R's output for binomial regression
What you have done is logistic regression. This can be done in basically any statistical software, and the output will be similar (at least in content, albeit the presentation may differ). There is |
4,496 | Interpretation of R's output for binomial regression | Call: This is just the call that you made to the function. It will be the exact same code you typed into R. This can be helpful for seeing if you made any typos.
(Deviance) Residuals: You can pretty much ignore these for logistic regression. For Poisson or linear regression, you want these to be more-or-less normally d... | Interpretation of R's output for binomial regression | Call: This is just the call that you made to the function. It will be the exact same code you typed into R. This can be helpful for seeing if you made any typos.
(Deviance) Residuals: You can pretty m | Interpretation of R's output for binomial regression
Call: This is just the call that you made to the function. It will be the exact same code you typed into R. This can be helpful for seeing if you made any typos.
(Deviance) Residuals: You can pretty much ignore these for logistic regression. For Poisson or linear reg... | Interpretation of R's output for binomial regression
Call: This is just the call that you made to the function. It will be the exact same code you typed into R. This can be helpful for seeing if you made any typos.
(Deviance) Residuals: You can pretty m |
4,497 | What are the main theorems in Machine (Deep) Learning? | As I wrote in the comments, this question seems too broad to me, but I'll make an attempt to an answer. In order to set some boundaries, I will start with a little math which underlies most of ML, and then concentrate on recent results for DL.
The bias-variance tradeoff is referred to in countless books, courses, MOOC... | What are the main theorems in Machine (Deep) Learning? | As I wrote in the comments, this question seems too broad to me, but I'll make an attempt to an answer. In order to set some boundaries, I will start with a little math which underlies most of ML, and | What are the main theorems in Machine (Deep) Learning?
As I wrote in the comments, this question seems too broad to me, but I'll make an attempt to an answer. In order to set some boundaries, I will start with a little math which underlies most of ML, and then concentrate on recent results for DL.
The bias-variance tr... | What are the main theorems in Machine (Deep) Learning?
As I wrote in the comments, this question seems too broad to me, but I'll make an attempt to an answer. In order to set some boundaries, I will start with a little math which underlies most of ML, and |
4,498 | What are the main theorems in Machine (Deep) Learning? | I think the following theorem that you allude to is considered to be pretty fundamental in statistical learning.
Theorem (Vapnik and Chervonenkis, 1971) Let $H$ be a
hypothesis class of functions from a domain $X$ to $\{0, 1\}$ and let the loss function
be the $0 − 1$ loss. Then, the following are equivalent:
$H$ has ... | What are the main theorems in Machine (Deep) Learning? | I think the following theorem that you allude to is considered to be pretty fundamental in statistical learning.
Theorem (Vapnik and Chervonenkis, 1971) Let $H$ be a
hypothesis class of functions from | What are the main theorems in Machine (Deep) Learning?
I think the following theorem that you allude to is considered to be pretty fundamental in statistical learning.
Theorem (Vapnik and Chervonenkis, 1971) Let $H$ be a
hypothesis class of functions from a domain $X$ to $\{0, 1\}$ and let the loss function
be the $0 −... | What are the main theorems in Machine (Deep) Learning?
I think the following theorem that you allude to is considered to be pretty fundamental in statistical learning.
Theorem (Vapnik and Chervonenkis, 1971) Let $H$ be a
hypothesis class of functions from |
4,499 | What are the main theorems in Machine (Deep) Learning? | The Kernel Trick is a general idea that's used in a lot of places, and comes from a lot of abstract maths about Hilbert Spaces. Way too much theory for me to type (copy...) out into an answer here, but if you skim through this you can get a good idea of its rigorous underpinnings:
http://www.stats.ox.ac.uk/~sejdinov/te... | What are the main theorems in Machine (Deep) Learning? | The Kernel Trick is a general idea that's used in a lot of places, and comes from a lot of abstract maths about Hilbert Spaces. Way too much theory for me to type (copy...) out into an answer here, bu | What are the main theorems in Machine (Deep) Learning?
The Kernel Trick is a general idea that's used in a lot of places, and comes from a lot of abstract maths about Hilbert Spaces. Way too much theory for me to type (copy...) out into an answer here, but if you skim through this you can get a good idea of its rigorou... | What are the main theorems in Machine (Deep) Learning?
The Kernel Trick is a general idea that's used in a lot of places, and comes from a lot of abstract maths about Hilbert Spaces. Way too much theory for me to type (copy...) out into an answer here, bu |
4,500 | What are the main theorems in Machine (Deep) Learning? | My favourite one is the Kraft inequality.
Theorem: For any description method $C$ for finite alphabet $A = \{1,\dots, m\}$, the code word lengths $L_C(1), \dots, L_C(2)$ must satisfy the inequality $\sum_{x \in A} 2 ^{-L_C(x)} \leq 1$.
This inequality relates compression with probability densities: given a code, the ... | What are the main theorems in Machine (Deep) Learning? | My favourite one is the Kraft inequality.
Theorem: For any description method $C$ for finite alphabet $A = \{1,\dots, m\}$, the code word lengths $L_C(1), \dots, L_C(2)$ must satisfy the inequality $ | What are the main theorems in Machine (Deep) Learning?
My favourite one is the Kraft inequality.
Theorem: For any description method $C$ for finite alphabet $A = \{1,\dots, m\}$, the code word lengths $L_C(1), \dots, L_C(2)$ must satisfy the inequality $\sum_{x \in A} 2 ^{-L_C(x)} \leq 1$.
This inequality relates com... | What are the main theorems in Machine (Deep) Learning?
My favourite one is the Kraft inequality.
Theorem: For any description method $C$ for finite alphabet $A = \{1,\dots, m\}$, the code word lengths $L_C(1), \dots, L_C(2)$ must satisfy the inequality $ |
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