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8800 | 1 | 9035 | null | 3 | 403 | i'm making experiments using app. 5000 labeled dataset.i'm trying different supervised ML algorithm to evaluate the results.The vector size is 13 with the labels (totally 12 features+1 label) and i have 15 vector of labeled "flower" class. experiments consist of all data set using 10k cross validation. All features are... | Interpretation of "one" feature change in a supervised classifier | CC BY-SA 2.5 | null | 2011-03-26T16:37:30.610 | 2011-04-01T11:26:39.950 | 2011-03-31T17:16:29.933 | 3270 | 3270 | [
"machine-learning",
"classification",
"feature-selection",
"dataset"
] |
8802 | 2 | null | 8695 | 4 | null | In case richiemorrisroe's response doesn't give you quite enough, I suggest...
- right-clicking your SPSS factor
analysis output and choosing Results
Coach to clarify the contents of the Variance
Explained table
- searching the Help files or
Tutorial for Reliability Analysis.
I'm thinking that by "composite... | null | CC BY-SA 2.5 | null | 2011-03-26T17:51:56.140 | 2011-03-26T17:51:56.140 | null | null | 2669 | null |
8803 | 2 | null | 8797 | 3 | null | IME, the confidence interval is useful as it gives a sense of how uncertain your estimates are. Its a good way to check how variable your results might be, and to give others a sense for how likely the results are to be within a particular range.
That being said, the typical interpretation of one is that 95 (for a 95% ... | null | CC BY-SA 2.5 | null | 2011-03-26T18:49:30.687 | 2011-03-26T18:49:30.687 | null | null | 656 | null |
8804 | 2 | null | 8375 | 2 | null | As I see it there are two survival analysis paradigms that could be used. The Cox regression framework allows time varying covariates and would produce an estimate for the risk of cancellation conditioned on any particular set of covariates relative to the mean level of cancellation. The glm framework with Poisson err... | null | CC BY-SA 2.5 | null | 2011-03-26T19:03:42.100 | 2011-03-26T19:03:42.100 | null | null | 2129 | null |
8805 | 2 | null | 8799 | 6 | null | There are a number of ways you can approach this problem (as chl has noted) and you should definitely read the links he gives to other questions.
That being said, here is some advice which you may find useful.
The psych package is quite good for simple analysis of questionnaries.
Download this using install.packages("p... | null | CC BY-SA 2.5 | null | 2011-03-26T19:04:16.593 | 2011-03-26T19:04:16.593 | null | null | 656 | null |
8806 | 2 | null | 8375 | 1 | null | Thank you for the clarification, B_Miner. I don't do a lot of forecasting myself, so take what follows with a pinch of salt. Here is what I would do as at least a first cut at the data.
- First, formulate and estimate a model that explains your TVCs. Do all of the cross-validation, error checking, etc., to make sure y... | null | CC BY-SA 2.5 | null | 2011-03-26T20:40:48.077 | 2011-03-26T20:40:48.077 | null | null | 3265 | null |
8807 | 1 | 8832 | null | 40 | 12823 | I've been using the [caret package](http://cran.r-project.org/web/packages/caret/index.html) in R to build predictive models for classification and regression. Caret provides a unified interface to tune model hyper-parameters by cross validation or boot strapping. For example, if you are building a simple 'nearest ne... | Cross-validating time-series analysis | CC BY-SA 2.5 | null | 2011-03-26T20:50:33.563 | 2022-07-12T14:24:45.640 | 2011-03-27T17:59:44.317 | 2817 | 2817 | [
"r",
"time-series",
"cross-validation"
] |
8808 | 1 | null | null | 1 | 455 | How do you derive the expression for the $100(1-\alpha)$% bayesian confidence interval when working with the uniform distribution in the interval $[-\theta,\theta]$?
| Derivation of bayesian confidence interval | CC BY-SA 2.5 | null | 2011-03-26T20:51:44.430 | 2011-04-29T01:06:26.093 | 2011-04-29T01:06:26.093 | 3911 | null | [
"bayesian",
"self-study"
] |
8809 | 2 | null | 8795 | 5 | null | I'm pretty sure the answer is yes, the standard binomial 'fair coin' test is still valid: if you wish to test whether two of the three probabilities of a [multinomial distribution](http://en.wikipedia.org/wiki/Multinomial_distribution) are the same but you're not interested in any hypotheses about the third probability... | null | CC BY-SA 2.5 | null | 2011-03-26T22:13:15.587 | 2011-03-26T22:13:15.587 | null | null | 449 | null |
8811 | 2 | null | 7321 | 1 | null | A null hypothesis of your significance test could be that the number of fields overlapping an $x$ point of the axis ($fields(x)$) comes from the same $k$ distribution no matter which point of the axis we take, i.e. $fields(x) \sim K$, where $K$ may take the values $0, 1, 2, ... \infty$. The alternative hypothesis could... | null | CC BY-SA 2.5 | null | 2011-03-26T23:42:45.120 | 2011-03-26T23:55:53.190 | 2011-03-26T23:55:53.190 | 3911 | 3911 | null |
8812 | 1 | 8826 | null | 12 | 5177 | What is the Jeffreys prior for the geometric distribution?
| Jeffreys prior for geometric distribution? | CC BY-SA 2.5 | null | 2011-03-27T01:37:04.537 | 2015-11-04T13:39:21.083 | 2011-04-29T00:56:16.210 | 3911 | 3911 | [
"distributions",
"bayesian",
"prior"
] |
8813 | 2 | null | 8807 | 5 | null | If you have time series data then you might have a "degrees of freedom problem" . For example if you have 4 observations taken at hourly intervals and then decide to use 241 observations at 1minute intervals, you have 241 observations but they are not necessarily independent. When you submit these 241 values/measuremen... | null | CC BY-SA 2.5 | null | 2011-03-27T01:49:23.647 | 2011-03-27T18:40:07.790 | 2011-03-27T18:40:07.790 | 2116 | 3382 | null |
8814 | 2 | null | 8791 | 1 | null | There might be a bit of confusion here with some imprecise statistical jargon. If you have data points that have been measured/reported with different precision/reliability/variability then one turns naturally to Generalized Least Squares where one transforms/weights the data by adjusting for the relative variability .... | null | CC BY-SA 2.5 | null | 2011-03-27T02:11:59.317 | 2011-03-27T11:15:02.570 | 2017-04-13T12:44:29.013 | -1 | 3382 | null |
8816 | 2 | null | 8795 | 3 | null | If you frame this as a binomial problem (p, 1-p), not a multinomial problem, you'll only be able to describe the past. You won't be able to say anything about the future. Why? Your removal of the middle "edge flips" is implied in your regrouping of the data.
In other words, your "data described" probability "p" of a... | null | CC BY-SA 2.5 | null | 2011-03-27T03:21:38.070 | 2011-03-27T22:54:06.670 | 2011-03-27T22:54:06.670 | 2775 | 2775 | null |
8817 | 1 | 8833 | null | 54 | 15155 | I am considering using Python libraries for doing my Machine Learning experiments. Thus far, I had been relying on WEKA, but have been pretty dissatisfied on the whole. This is primarily because I have found WEKA to be not so well supported (very few examples, documentation is sparse and community support is less than ... | Machine Learning using Python | CC BY-SA 4.0 | null | 2011-03-27T04:00:59.400 | 2018-10-30T07:06:54.283 | 2018-10-30T07:06:54.283 | 128677 | 3301 | [
"machine-learning",
"python"
] |
8818 | 1 | 8825 | null | 12 | 9202 | What is the relation between a dimension and a component in a Gaussian Mixture Model? And what are the meanings of dimension and component? Thank you.
Please correct me if Im wrong: my understanding is the observed data have many dimensions. Each dimension represents a feature/aspect of the collected data and has its o... | What's a component in gaussian mixture model? | CC BY-SA 2.5 | null | 2011-03-27T04:17:46.557 | 2011-04-08T20:43:53.850 | 2011-04-08T20:43:53.850 | 919 | 2729 | [
"multivariate-analysis",
"normal-distribution",
"mixture-distribution"
] |
8819 | 2 | null | 8817 | 14 | null | In terms of working with text, have a look at NLTK. Very, very well supported & documented (there's even a book online, or in paper if you prefer) and will do the preprocesing you require. You might find Gensim useful as well; the emphasis is on vector space modeling and it's got scalable implementations of LSI and LDA... | null | CC BY-SA 2.5 | null | 2011-03-27T04:41:29.610 | 2011-03-27T04:41:29.610 | null | null | 26 | null |
8820 | 1 | 8831 | null | 3 | 1181 | I have a data sample (in this case an EEG data sample, but my question refers to any type of data samples of prior unknown distributions).
I would like to do a nonparametric estimate of the expected value for my sample. I did some research, from what I understood I can do this using bootstrap sampling. I found a pdf [h... | Nonparametric expected value estimation of sample from unknown distribution | CC BY-SA 2.5 | null | 2011-03-27T04:42:00.160 | 2011-03-27T10:59:28.920 | null | null | null | [
"estimation",
"sampling",
"nonparametric",
"markov-chain-montecarlo",
"bootstrap"
] |
8821 | 2 | null | 8817 | 3 | null | Check out [libsvm](http://www.csie.ntu.edu.tw/~cjlin/libsvm/).
| null | CC BY-SA 2.5 | null | 2011-03-27T05:23:07.833 | 2011-03-27T05:23:07.833 | null | null | 364 | null |
8822 | 2 | null | 2547 | 3 | null | I don't think there is a good descriptive reason for choosing median over mean for age distributions. There is one of practicality when comparing reported data.
Many countries report their population in 5-year age intervals with the top band open-ended. This causes some difficulties calculating the mean from the in... | null | CC BY-SA 3.0 | null | 2011-03-27T05:39:30.573 | 2012-01-02T22:59:09.940 | 2012-01-02T22:59:09.940 | 930 | 2958 | null |
8823 | 1 | 436376 | null | 23 | 1603 | This an exercise given in Probability Theory: The Logic of Science by Edwin Jaynes, 2003. There is a partial solution [here](http://ksvanhorn.com/bayes/Papers/sanders.pdf). I have worked out a more general partial solution, and was wondering if anyone else has solved it. I will wait a bit before posting my answer, t... | Has anyone solved PTLOS exercise 4.1? | CC BY-SA 2.5 | null | 2011-03-27T08:36:41.173 | 2021-12-08T09:59:14.283 | null | null | 2392 | [
"independence",
"likelihood-ratio",
"hypothesis-testing",
"multiple-comparisons"
] |
8824 | 2 | null | 8817 | 9 | null | Python has a wide range of ML libraries (check out mloss.org as well). However, I always have the feeling that it's more of use for ml researchers than for ml practitioners.
[Numpy/SciPy](http://scipy.org) and [matplotlib](http://matplotlib.sf.net) are excellent tools for scientific work with Python. If you are not afr... | null | CC BY-SA 2.5 | null | 2011-03-27T08:55:32.053 | 2011-03-27T08:55:32.053 | null | null | 2860 | null |
8825 | 2 | null | 8818 | 11 | null | A mixture of Gaussians is defined as a linear combination of multiple Gaussian distributions. Thus it has multiple modes. The dimension refers to the data (e.g. the color, length, width, height and material of a shoe) while the number of components refers to the model. Each Gaussian in your mixture is one component. Th... | null | CC BY-SA 2.5 | null | 2011-03-27T08:59:37.747 | 2011-03-27T08:59:37.747 | null | null | 2860 | null |
8826 | 2 | null | 8812 | 16 | null | The geometric distribution is given by:
$$p(X|\theta)=(1-\theta)^{X-1}\theta \;\;\; X=1,2,3,\dots$$
The log likelihood is thus given by:
$$\log[p(X|\theta)]=L=(X-1)\log(1-\theta)+\log(\theta)$$
Differentiate once:
$$\frac{\partial L}{\partial \theta}=\frac{1}{\theta}-\frac{X-1}{1-\theta}$$
And again:
$$\frac{\partial^{... | null | CC BY-SA 3.0 | null | 2011-03-27T08:59:51.443 | 2013-10-25T08:14:19.340 | 2013-10-25T08:14:19.340 | 17230 | 2392 | null |
8827 | 2 | null | 7430 | 2 | null | @JMS answer is adequate for the nuts and bolts of changing variables. However, [This question](https://stats.stackexchange.com/questions/8104/why-is-a-p-sigma2-sim-textig0-001-0-001-prior-on-variance-considered-we) may help you a bit with why it is uniform on that scale.
My answer to [this question](https://stats.stac... | null | CC BY-SA 2.5 | null | 2011-03-27T09:09:00.470 | 2011-03-27T09:09:00.470 | 2017-04-13T12:44:49.683 | -1 | 2392 | null |
8828 | 2 | null | 8817 | 4 | null | Not sure if this is particularly useful, but there's a guide for programmers to learn statistics in Python available online. [http://www.greenteapress.com/thinkstats/](http://www.greenteapress.com/thinkstats/)
It seems pretty good from my brief scan, and it appears to talk about some machine learning methods, so it mig... | null | CC BY-SA 2.5 | null | 2011-03-27T09:10:04.543 | 2011-03-27T09:10:04.543 | null | null | 656 | null |
8829 | 2 | null | 8808 | 3 | null | So you have a density of:
$$p(X_i|\theta)=\frac{1}{2\theta}\;\;\;\; X_i\in[-\theta,\theta]$$
Now this is what is called a scale density, and $\theta$ is a scale parameter, just like the standard deviation in a normal distribution.
Now to do a Bayesian CI you require a prior distribution for $\theta$. Because $\theta$ ... | null | CC BY-SA 2.5 | null | 2011-03-27T09:43:27.663 | 2011-03-27T09:43:27.663 | null | null | 2392 | null |
8830 | 2 | null | 8817 | 8 | null | Let me suggest [Orange](http://orange.biolab.si/)
>
comprehensive
Yes
>
scalable (100k features, 10k examples)
Yes
>
well supported libraries for doing ML in Python out there?
Yes
>
library that has a good collection of classifiers, feature selection methods (Information Gain, Chi-Sqaured etc.),
All of t... | null | CC BY-SA 2.5 | null | 2011-03-27T10:03:22.980 | 2011-03-27T10:03:22.980 | null | null | 1496 | null |
8831 | 2 | null | 8820 | 2 | null | It is quite simple; you make a subsample by sampling with replacement:
```
sample(x,replace=T)
```
calculate the statistic you want on it:
```
mean(sample(x,replace=T))
```
finally average it over many repetitions:
```
mean(replicate(1000,mean(sample(x,replace=T)))
```
| null | CC BY-SA 2.5 | null | 2011-03-27T10:51:10.953 | 2011-03-27T10:59:28.920 | 2011-03-27T10:59:28.920 | null | null | null |
8832 | 2 | null | 8807 | 11 | null | The "classical" k-times cross-validation technique is based on the fact that each sample in the available data set is used (k-1)-times to train a model and 1 time to test it. Since it is very important to validate time series models on "future" data, this approach will not contribute to the stability of the model.
One ... | null | CC BY-SA 2.5 | null | 2011-03-27T11:19:52.957 | 2011-03-28T14:49:33.693 | 2011-03-28T14:49:33.693 | 1496 | 1496 | null |
8833 | 2 | null | 8817 | 40 | null | About the scikit-learn option: 100k (sparse) features and 10k samples is reasonably small enough to fit in memory hence perfectly doable with scikit-learn (same size as the 20 newsgroups dataset).
Here is a tutorial I gave at PyCon 2011 with a chapter on text classification with exercises and solutions:
- http://sciki... | null | CC BY-SA 2.5 | null | 2011-03-27T11:20:59.597 | 2011-03-27T11:35:10.863 | 2011-03-27T11:35:10.863 | 2150 | 2150 | null |
8834 | 2 | null | 8732 | 0 | null | Whether or not you wish to forecast or not has nothing whatsoever to do with correct time series analysis. Time series methods can develop a robust model which can be used simply to characterize the relationship between a dependent series and a set of user-suggested inputs (a.k.a. user-specified predictor series) and e... | null | CC BY-SA 2.5 | null | 2011-03-27T13:06:33.083 | 2011-03-27T13:06:33.083 | null | null | 3382 | null |
8837 | 2 | null | 8744 | 6 | null | I suggest a two-step approach:
- get a good initial estimates of the cluster centers, e.g. using hard or fuzzy K-means.
- Use Global Nearest Neighbor assignment to associate points with cluster centers: Calculate a distance matrix between each point and each cluster center (you can make the problem a bit smaller by... | null | CC BY-SA 2.5 | null | 2011-03-27T14:00:14.600 | 2011-03-27T14:28:15.977 | 2011-03-27T14:28:15.977 | 198 | 198 | null |
8838 | 2 | null | 8568 | 1 | null | You could rewrite your model in a Bayesian software (OpenBUGS, PyMC).
When any new information is available add them to the model and re-estimate the posterior.
| null | CC BY-SA 2.5 | null | 2011-03-27T14:26:36.253 | 2011-03-27T14:26:36.253 | null | null | 3911 | null |
8839 | 2 | null | 7197 | 1 | null | You could set up a model that predicts LOS using YEAR, DG and other variables available (hospital datasets usually include age, gender and many other potential predictors).
One way of comparing your hospital to the comparison set is joining the two datasets and adding a hospital column (either 'my hospital' or 'compari... | null | CC BY-SA 2.5 | null | 2011-03-27T14:57:41.187 | 2011-03-27T14:57:41.187 | null | null | 3911 | null |
8840 | 2 | null | 8817 | 3 | null | SHOGUN ([将軍](http://www.shogun-toolbox.org/)) is a large scale machine learning toolbox, which seems promising.
| null | CC BY-SA 2.5 | null | 2011-03-27T15:16:27.763 | 2011-03-27T15:16:27.763 | null | null | 1351 | null |
8841 | 5 | null | null | 0 | null | Mixture models arise in attempts to characterize complicated probability distributions, especially those with two or more modes, in terms of distributions with mathematically simple descriptions.
### Disambiguation
- Do not confuse a "mixture model" with a "mixed model"! The former concerns distributions, typicall... | null | CC BY-SA 4.0 | null | 2011-03-27T16:20:20.390 | 2020-05-02T09:09:50.780 | 2020-05-02T09:09:50.780 | 1352 | 919 | null |
8842 | 4 | null | null | 0 | null | A mixture distribution is one that is written as a convex combination of other distributions. Use the "compound-distributions" tag for "concatenations" of distributions (where a parameter of a distribution is itself a random variable). | null | CC BY-SA 4.0 | null | 2011-03-27T16:20:20.390 | 2020-05-02T09:01:39.847 | 2020-05-02T09:01:39.847 | 1352 | 919 | null |
8844 | 1 | 8860 | null | 10 | 3462 | How to calculate discrete interval coverage?
What I know how to do:
If I had a continuous model, I could define a 95% confidence interval for each of my predicted values, and then see how often the actual values were within the confidence interval. I might find that only 88% of the time did my 95% confidence interval... | Discrete functions: Confidence interval coverage? | CC BY-SA 2.5 | null | 2011-03-27T17:31:46.470 | 2011-12-12T13:59:11.507 | null | null | 3919 | [
"confidence-interval",
"discrete-data"
] |
8845 | 1 | 8861 | null | 6 | 241 | [Samiuddin, (1976)](http://www.jstor.org/stable/2285344) states:
or, typset with $\LaTeX$ as originally posted
>
We start with the usual noninformative
prior distribution of $\mu_i$ and
$\sigma_i (i = 1,2,\ldots, k)$
$$\pi(\mu_1, \mu_2, \ldots, \... | What does $d$ mean in this notation of the "usual noninformative prior of $\mu_i$ and $\sigma_i$?" | CC BY-SA 3.0 | null | 2011-03-27T17:46:18.863 | 2011-05-18T13:45:14.577 | 2011-05-18T13:45:14.577 | 1381 | 1381 | [
"probability",
"bayesian",
"prior",
"notation"
] |
8846 | 1 | 8863 | null | 6 | 4852 | I'm about to apply Kruskal-Wallis test (non-parametric ANOVA) on three groups of unequal length. I was taught/advised to apply Krukal-Wallis only if:
- dependent variable is at least at ordinal level of measurement
- group's $ n > 5 $ (otherwise H statistic is not $ \chi ^2 $ distributed, so exact p-value cannot be c... | Kruskal-Wallis test data considerations | CC BY-SA 2.5 | null | 2011-03-27T19:12:32.223 | 2011-03-28T02:25:16.240 | 2011-03-27T21:11:14.717 | 1356 | 1356 | [
"r",
"nonparametric",
"kruskal-wallis-test"
] |
8847 | 2 | null | 8807 | 11 | null | [http://robjhyndman.com/researchtips/crossvalidation/](http://robjhyndman.com/researchtips/crossvalidation/) contains a quick tip for cross validation of time series. Regarding using random forest for time series data....not sure although it seems like an odd choice given that the model is fitted using bootstrap sample... | null | CC BY-SA 2.5 | null | 2011-03-27T19:20:57.207 | 2011-03-27T19:20:57.207 | null | null | 2040 | null |
8849 | 2 | null | 8846 | 0 | null |
- You need not check homoscedasticity. Kruskal and Wallis stated in their original paper that the “test may be fairly insensitive to differences in variability”.
- If there is no exact test available, you can use bootstrap.
| null | CC BY-SA 2.5 | null | 2011-03-27T19:34:23.447 | 2011-03-27T23:12:57.357 | 2011-03-27T23:12:57.357 | 3911 | 3911 | null |
8851 | 2 | null | 8664 | 1 | null | When you include subject as a random effect in ANOVA you assume that the subject effect and the product effect are additive. Have you thought about negative covariances? Maybe the more one likes red socks the less they like green ones...
| null | CC BY-SA 2.5 | null | 2011-03-27T20:16:46.323 | 2011-03-27T20:16:46.323 | null | null | 3911 | null |
8852 | 2 | null | 4150 | 1 | null | Write down the complete likelihood, take the derivative and do a gradient based optimization.
You can do this online very easily (that is, process one point after the other) and this might result in far faster convergence than EM if the redundancy in your data is high.
| null | CC BY-SA 2.5 | null | 2011-03-27T20:36:29.907 | 2011-03-27T20:36:29.907 | null | null | 2860 | null |
8853 | 2 | null | 8653 | 2 | null | I understand you have
- many brain imaging datasets
- classified into 2 groups, study and control
- image processing methods
- parameters of the image processing to tune
- a collection of processed images with various parameter settings
and that you will
- run a new study recruiting similar subjects and control... | null | CC BY-SA 2.5 | null | 2011-03-27T20:51:45.013 | 2011-03-27T20:51:45.013 | null | null | 3911 | null |
8854 | 1 | 8857 | null | 2 | 6453 | I'm interested in determining whether two or more groups of data share the same mean, and it seems like the ANOVA framework is a good way to approach this. However, ANOVA assumes residuals are normally distributed, while each of my data is a number between 0 and 100 (a percentage). Because normal distributions have sup... | ANOVA-like test for bounded variables (percentages) | CC BY-SA 3.0 | null | 2011-03-27T21:15:26.893 | 2012-06-25T06:16:16.217 | 2012-06-25T06:16:16.217 | 183 | 3921 | [
"hypothesis-testing",
"anova"
] |
8856 | 2 | null | 8633 | 2 | null | Your covariate is not only different across subjects, but also gets different during the multiple measurements on the same subject. This has implications on the study design and the analysis method as well.
(I'm not sure if you really meant the effect of the covariate in your second sentence.)
Study design: if your mul... | null | CC BY-SA 2.5 | null | 2011-03-27T21:28:27.603 | 2011-03-27T21:28:27.603 | null | null | 3911 | null |
8857 | 2 | null | 8854 | 4 | null | Is the percentage the most raw data you have, or did you compute the percentage from some sort of binomial count data? If the latter, then you should submit the raw 1s and 0s to a logistic regression. In R, check out glm:
```
glm(
response ~ group
, family = binomial
)
```
If each individual of each group cont... | null | CC BY-SA 2.5 | null | 2011-03-27T21:36:20.393 | 2011-03-27T21:36:20.393 | null | null | 364 | null |
8858 | 2 | null | 8854 | 2 | null | Percentage values may have normal distributions, e.g. cholesterol levels across humans is approximately normally distributed, and it remains normal even if expressed as percentage of the maximal cholesterol level seen. In such cases you need not worry about the fact that your data cover only a narrow interval.
However,... | null | CC BY-SA 2.5 | null | 2011-03-27T21:56:14.757 | 2011-03-27T21:56:14.757 | null | null | 3911 | null |
8859 | 1 | null | null | 0 | 2322 | I am currently implementing a text classification program with Naive Bayes. I produce two multinominal models in my training function: p(w|nonSPAM) and p(w|SPAM)) as well as a prior probability P(S).
In my testing function I go through each test document, and for each test document, I go through all the terms and compu... | How to convert log likelihoods into scores in Naive Bayes? | CC BY-SA 2.5 | null | 2011-03-27T22:13:20.827 | 2011-04-08T20:43:40.633 | 2017-05-23T12:39:26.167 | -1 | null | [
"machine-learning",
"maximum-likelihood",
"naive-bayes"
] |
8860 | 2 | null | 8844 | 7 | null | Neyman's confidence intervals make no attempt to provide coverage of the parameter in the case of any particular interval. Instead they provide coverage over all possible parameter values in the long run. In a sense they attempt to be globally accurate at the expense of local accuracy.
Confidence intervals for binomial... | null | CC BY-SA 3.0 | null | 2011-03-27T22:45:57.677 | 2011-12-12T13:59:11.507 | 2011-12-12T13:59:11.507 | 1036 | 1679 | null |
8861 | 2 | null | 8845 | 7 | null | This is shorthand notation for a "differential" of the mean and variance parameters. The longhand version goes:
$$p(\mu\in[\mu_1,\mu_1+d\mu_1)|I)\propto d\mu_1$$
This indicates a uniform probability with respect to $\mu$. A more familiar notation is:
$$p(\mu|I)\propto 1$$
It comes from the "proper" derivation of a PD... | null | CC BY-SA 2.5 | null | 2011-03-27T22:58:10.103 | 2011-03-28T09:27:14.167 | 2011-03-28T09:27:14.167 | 2392 | 2392 | null |
8862 | 2 | null | 8818 | 3 | null | A mixture of Gaussians algorithm is a probabilistic generalization of the $k$-means algorithm. Each mean vector in $k$-means is component. The number of elements in each of the $k$ vectors is the dimension of the model. Thus, if you have $n$ dimensions, you have a $k\times n$ matrix of mean vectors.
It is no different ... | null | CC BY-SA 2.5 | null | 2011-03-28T02:16:21.630 | 2011-03-28T19:37:08.190 | 2011-03-28T19:37:08.190 | 2660 | 2660 | null |
8863 | 2 | null | 8846 | 9 | null | With small, and possibly unequal group sizes, I'd go with chl's and onestop's suggestion and do a Monte-Carlo permutation test. For the permutation test to be valid, you need exchangeability under $H_{0}$. If all distributions have the same shape (and are therefore identical under $H_{0}$), this is true.
Here's a first... | null | CC BY-SA 2.5 | null | 2011-03-28T02:17:44.203 | 2011-03-28T02:25:16.240 | 2011-03-28T02:25:16.240 | 1909 | 1909 | null |
8864 | 1 | null | null | 5 | 743 | I'm new to predictive models and I have a problem at hand that I need some advice with. Basically for a clinical application we want to predict the outcome of a rating scale with a model built on top of outcomes of our new measurement device. My dependent variable, a clinical rating scale, is an integer between 0 and 1... | Looking for ideas to build a predictive model | CC BY-SA 2.5 | null | 2011-03-28T02:44:24.290 | 2011-03-29T19:28:12.003 | null | null | 2020 | [
"r",
"regression",
"predictive-models"
] |
8866 | 2 | null | 8405 | 1 | null | As an alternative to the `Hmisc` package, you can use `table` or `xtabs`:
```
table(cut(X$Age, c(0, 27, 37, 47, 999), X$Outcome)
xtabs(~ cut(Age, c(0, 27, 37, 47, 999) + Outcome, data=X)
```
| null | CC BY-SA 2.5 | null | 2011-03-28T06:07:52.767 | 2011-03-28T06:07:52.767 | null | null | 1569 | null |
8867 | 1 | null | null | 21 | 771 | Given the following hierarchical model,
$$
X \sim {\mathcal N}(\mu,1),
$$
and,
$$
\mu \sim {\rm Laplace}(0, c)
$$
where $\mathcal{N}(\cdot,\cdot)$ is a normal distribution. Is there a way to get an exact expression for the Fisher information of the marginal distribution of $X$ given $c$. That is, what is the Fisher inf... | Fisher information in a hierarchical model | CC BY-SA 3.0 | null | 2011-03-28T06:33:55.567 | 2020-08-07T16:47:16.740 | 2020-08-07T16:47:16.740 | 7290 | 530 | [
"multilevel-analysis",
"fisher-information"
] |
8868 | 1 | 8870 | null | 28 | 59160 | When doing time series research in R, I found that `arima` provides only the coefficient values and their standard errors of fitted model. However, I also want to get the p-value of the coefficients.
I did not find any function that provides the significance of coef.
So I wish to calculate it by myself, but I don't k... | How to calculate the p-value of parameters for ARIMA model in R? | CC BY-SA 2.5 | null | 2011-03-28T09:19:01.760 | 2020-09-27T06:24:55.793 | 2020-09-27T06:24:55.793 | 7290 | 3926 | [
"r",
"time-series",
"chi-squared-test",
"arima"
] |
8869 | 1 | 8871 | null | 6 | 858 | After knowing how LSA works, I went on continue reading on pLSA but couldn't really make sense of the mathematical formula. This is what I get from [wikipedia](http://en.wikipedia.org/wiki/Probabilistic_latent_semantic_analysis) (other academic papers/tutorial show similar form)
\begin{align}
P(w,d) & = \sum_{c} P(c) P... | Deriving mathematical model of pLSA | CC BY-SA 3.0 | null | 2011-03-28T09:21:13.490 | 2015-09-25T06:48:48.613 | 2015-09-25T06:48:48.613 | 3837 | 3837 | [
"machine-learning",
"probability",
"bayesian",
"multilevel-analysis",
"latent-semantic-analysis"
] |
8870 | 2 | null | 8868 | 5 | null | The "t value" is the ratio of the coefficient to the standard error. The degrees of freedom (ndf) would be the number of observations minus the max order of difference in the model minus the number of estimated coefficients. The "F value " would be the square of the "t value" In order to exactly compute probability you... | null | CC BY-SA 2.5 | null | 2011-03-28T09:36:22.263 | 2011-03-28T09:36:22.263 | null | null | 3382 | null |
8871 | 2 | null | 8869 | 6 | null | I am assuming you want to derive:
\begin{align*}
P(w,d) = \sum_{c} P(c) P(d|c) P(w|c)
&= P(d) \sum_{c} P(c|d) P(w|c)
\end{align*}
Further, this is similar to Probabilistic latent semantic indexing (cf. Blei, Jordan, and Ng (2003) Latent Dirichlet Allocation. JMLR section 4.3). PLSI posits that a document label $d$ and... | null | CC BY-SA 2.5 | null | 2011-03-28T11:24:36.273 | 2011-03-28T11:53:06.290 | 2011-03-28T11:53:06.290 | 2116 | 1307 | null |
8872 | 2 | null | 8868 | 27 | null | Since `arima` uses maximum likelihood for estimation, the coefficients are assymptoticaly normal. Hence divide coefficients by their standard errors to get the z-statistics and then calculate p-values. Here is the example with in R with the first example from `arima` help page:
```
> aa <- arima(lh, order = c(1,0,0))
... | null | CC BY-SA 3.0 | null | 2011-03-28T11:41:48.280 | 2013-05-06T10:22:17.483 | 2013-05-06T10:22:17.483 | 2116 | 2116 | null |
8873 | 1 | null | null | 3 | 600 | How to calculate sample size needed for GWAS for a given MAF, power, $p$-value and frequency of the disease ?
| Sample size in genome-wide studies | CC BY-SA 2.5 | null | 2011-03-28T13:24:08.807 | 2011-03-28T20:05:23.343 | 2011-03-28T14:17:07.833 | 930 | 3870 | [
"genetics",
"statistical-power"
] |
8874 | 2 | null | 8797 | 4 | null | Based on my calculations, it seems that you had about 16 or 17 participants. The typical methods for calculating confidence intervals of means assume that sample means are normally distributed. In the case of very skewed distributions, that assumption is only valid for large samples, which rules of thumb define as at l... | null | CC BY-SA 2.5 | null | 2011-03-28T13:54:58.437 | 2011-03-28T13:54:58.437 | null | null | 3874 | null |
8875 | 2 | null | 8869 | 3 | null | The line $P(c|d)P(c) = P(d|c)P(c)$ (your eq 2) should be $P(c|d)P(d) = P(d|c)P(c)$.
I'm not sure why you don't think Bayes theorem and basic probability rules are useful:
Eq 1 is Bayes theorem (ie recognizing that $P(d|c)P(c) = P(c,d)$ and plugging in to the definition of conditional probability)
Eq 2 follows immediat... | null | CC BY-SA 2.5 | null | 2011-03-28T13:56:59.857 | 2011-03-28T13:56:59.857 | null | null | 26 | null |
8876 | 2 | null | 8572 | 19 | null | So the simple answer is yes: Metropolis-Hastings and its special case Gibbs sampling :) General and powerful; whether or not it scales depends on the problem at hand.
I'm not sure why you think sampling an arbitrary discrete distribution is more difficult than an arbitrary continuous distribution. If you can calculate... | null | CC BY-SA 2.5 | null | 2011-03-28T14:24:43.273 | 2011-03-31T16:45:44.557 | 2011-03-31T16:45:44.557 | 26 | 26 | null |
8877 | 1 | null | null | 7 | 2928 | My [struggle](https://stats.stackexchange.com/questions/8846/kruskal-wallis-test-data-considerations) with non-parametric methods continues... I'd like to apply a median polish instead of two-way ANOVA (normality and homoscedascity assumptions are violated, and $ n_{ij} $ are small, so I can't use CLT as an excuse). I'... | Two-way robust ANOVA | CC BY-SA 2.5 | null | 2011-03-28T15:37:13.107 | 2011-03-28T22:54:48.707 | 2017-04-13T12:44:33.310 | -1 | 1356 | [
"r",
"anova",
"nonparametric",
"median",
"robust"
] |
8878 | 2 | null | 8877 | 2 | null | How is normality violated? Medians are more sensitive to skew than means as n gets low. Be careful of that. It would be very problematic if small n's varied in a systematic way.
How much is homoscedascity violated? If the n's are about equal it won't matter much for quite large differences.
| null | CC BY-SA 2.5 | null | 2011-03-28T16:13:24.807 | 2011-03-28T16:13:24.807 | null | null | 601 | null |
8880 | 2 | null | 8808 | 3 | null | The question is not totally clear, but I am going to assume that that you have an improper prior for $\theta$ proportional to $1/\theta$ and $n$ observed points $\{X_i\}$.
A sufficient statistic is $Y=\max_i |X_i|$, and you will have
$$Pr(Y \le y|\theta) = (y/\theta)^n \textrm{ if } 0 \le y \le \theta $$
so with dens... | null | CC BY-SA 2.5 | null | 2011-03-28T18:36:09.553 | 2011-03-29T21:02:58.523 | 2011-03-29T21:02:58.523 | 2958 | 2958 | null |
8881 | 1 | null | null | 1 | 1762 | I have a dataset with the following types of predictors:
- binary (e.g., gender),
- nominal with 3 categories,
- ordinal, and
- continuous
### Question:
What is the best way to set up a regression model that includes these different types of variable?
| How to perform a regression model with a mix of binary, nominal, ordinal, and continuous predictors? | CC BY-SA 2.5 | null | 2011-03-28T18:53:05.850 | 2016-07-24T11:34:16.580 | 2011-04-05T13:28:12.420 | 183 | 3472 | [
"regression"
] |
8882 | 1 | 8888 | null | 5 | 1729 | If one has dataset with a single outlier such as the following graph taken from Vanni-Mercer et al. (2009), is there a statistical test that one can use that accounts for the single outlier rather than having to throw it out or declare significance because of a single data point?
RT is reaction time. Trial rank is esse... | Alternative to Tukey's HSD | CC BY-SA 2.5 | null | 2011-03-28T19:45:31.427 | 2011-03-29T07:25:09.323 | null | null | 2660 | [
"post-hoc"
] |
8883 | 1 | 8921 | null | 4 | 2868 | I have a process which consists of a number of events and what is known is the timings between the events. What I'm trying to determine is a distribution that allows me to determine a likelyhood that a new sample fits the distibution.
The issue is mainly that if you have lots of samples you can approximate the result u... | Conditional expection of gamma distribution on sum | CC BY-SA 2.5 | null | 2011-03-28T19:53:17.083 | 2011-03-30T04:22:56.787 | 2011-03-29T16:18:23.670 | 3932 | 3932 | [
"conditional-probability",
"gamma-distribution"
] |
8884 | 1 | 8887 | null | 8 | 2694 | The central limit theorem as I am familiar with it applies to the limiting (rescaled) distribution of $n$ convolutions of a single probability distribution as $n$ goes to infinity, or equivalently, to distribution one gets from taking a sum of $n$ random variables each with a single fixed distribution. That is, it is a... | Central limit theorem for sum from varied distributions | CC BY-SA 2.5 | null | 2011-03-28T19:59:07.047 | 2011-03-30T03:34:38.853 | 2011-03-28T20:24:39.990 | 2116 | 2912 | [
"central-limit-theorem"
] |
8885 | 1 | 17070 | null | 5 | 195 | How to order a set of vectors $W$ if we are given a training set $V$ consisting of $k$ $n$-dimensional vectors and partial order of them? It is not the total order, so some vectors might not be comparable with some other. The answer will depend on assumptions, so feel free to make any reasonable assumptions.
Example
Le... | Prediction of an order of vectors using partially ordered set | CC BY-SA 2.5 | null | 2011-03-28T20:01:44.653 | 2011-10-16T12:32:23.090 | null | null | 1643 | [
"ordinal-data"
] |
8886 | 2 | null | 8873 | 2 | null | Haven't tried it myself, but you might like to try the [GWApower](http://www.stats.ox.ac.uk/~marchini/software.html#GWApower) R package. See [Spencer et al. 2009](http://dx.doi.org/10.1371/journal.pgen.1000477).
| null | CC BY-SA 2.5 | null | 2011-03-28T20:05:23.343 | 2011-03-28T20:05:23.343 | null | null | 449 | null |
8887 | 2 | null | 8884 | 7 | null | The [theorem 3.1](http://books.google.com/books?id=4LkdSaI4xXMC&lpg=PP1&dq=inauthor%3a%22Valentin%20Vladimirovich%20Petrov%22&hl=fr&pg=PA91#v=onepage&q&f=false) in this [book](http://books.google.com/books?id=4LkdSaI4xXMC&lpg=PP1&dq=inauthor%3A%22Valentin%20Vladimirovich%20Petrov%22&hl=fr&pg=PP1#v=onepage&q&f=false) an... | null | CC BY-SA 2.5 | null | 2011-03-28T20:18:16.797 | 2011-03-28T20:18:16.797 | null | null | 2116 | null |
8888 | 2 | null | 8882 | 4 | null | Typically in RT studies there's good reason to believe that the first trials are different qualitatively from the rest and the long RT is merely an indicator of that. Why would you want to bother keeping them?
| null | CC BY-SA 2.5 | null | 2011-03-28T20:20:14.667 | 2011-03-28T20:20:14.667 | null | null | 601 | null |
8890 | 2 | null | 8864 | 5 | null | If you use a regression model you may start with ordinal logistic regression since your dependent variable has an ordinal scale of 11 levels. Then you may want to look at the threshold values as you may find that they are equidistant (after some transformation), in which case you may go for linear regression.
Tree base... | null | CC BY-SA 2.5 | null | 2011-03-28T21:43:57.283 | 2011-03-29T19:28:12.003 | 2011-03-29T19:28:12.003 | 3911 | 3911 | null |
8891 | 1 | null | null | 8 | 10826 | I am trying to predict real estate sales prices.
- In my dataset there are independent variables that are both nominal and numeric (square meters, prices etc.)
- Before feeding the data to any regression algorithm I'd like to preprocess it correctly (binning, normalizing mean / std deviation, discretization etc.)
... | Data preparation for regression | CC BY-SA 2.5 | null | 2011-03-28T21:53:25.267 | 2011-03-29T14:11:32.650 | null | null | 3933 | [
"r",
"regression",
"predictive-models",
"standardization"
] |
8893 | 2 | null | 8882 | 2 | null | You might consider checking out the [gamm4](http://cran.r-project.org/web/packages/gamm4/index.html) package in R, which basically finds a non-linear function that fits the data while auto-penalizing complexity. I recently used it to fit a similar data set, then obtained the residuals and used these to bootstrap pretty... | null | CC BY-SA 2.5 | null | 2011-03-28T23:11:36.710 | 2011-03-28T23:11:36.710 | null | null | 364 | null |
8894 | 2 | null | 8885 | 0 | null | In the example if we order the vectors according to the first attribute the three pairwise comparisons will be satisfied. This is the same solution as what you suggested in your last sentence. Why aren't you satisfied with this solution? Do you have any further information on the problem?
| null | CC BY-SA 2.5 | null | 2011-03-28T23:39:05.167 | 2011-03-28T23:39:05.167 | null | null | 3911 | null |
8895 | 2 | null | 8891 | 1 | null | The real estate prices that you are tying to predict , are they consecutive/chronological values i.e. time series data or are they prices for different classes e.g. this years prices for different classes for the same time frame. You might want to read something I wrote on these two kinds of problems as it warns that ... | null | CC BY-SA 2.5 | null | 2011-03-28T23:57:31.773 | 2011-03-28T23:57:31.773 | null | null | 3382 | null |
8897 | 2 | null | 8891 | 1 | null | Binning your data is usually a bad idea because it will cause you to lose information, which will likely result in loss of power. Also, I would rarely standardise variables before doing regression, although some people may like to.
A really good book to read, if you can get it, is "Regression Modeling Strategies" by Fr... | null | CC BY-SA 2.5 | null | 2011-03-29T06:38:54.180 | 2011-03-29T06:38:54.180 | null | null | 3835 | null |
8898 | 1 | null | null | 6 | 857 | A pathologist friend came to me for help with the following question for a research project. The goal is to compare the effectiveness of three different diagnostic techniques. The data set is as follows: there are 50 different specimens, each specimen was evaluated by 4 pathologists, and 3 different instruments (ie 6... | How to compare the effectiveness of medical diagnostic techniques? | CC BY-SA 3.0 | null | 2011-03-29T06:56:06.277 | 2011-04-24T17:14:47.580 | 2011-04-14T02:01:51.063 | 3939 | 3939 | [
"hypothesis-testing",
"statistical-significance"
] |
8899 | 1 | 8906 | null | 0 | 239 | Suppose I have 2 variables
$A$:
$P(A) =$ 0.01
$P( \lnot A) =$ 0.99
And $B$ that depends on $A$:
$P(B|A) =$ 0.05
$P( \lnot B|A) =$ 0.95
$P(B| \lnot A) =$ 0.01
$P( \lnot B| \lnot A) =$ 0.99
Applying:
$$P(B)=\sum_{A}^{ } P(B|A)P(A)$$
we get
$P(B)=(0.01)(0.05)+(0.99)(0.01)=0.0104$
Ok, my question is the following:
I... | Several questions about conditional probability | CC BY-SA 2.5 | null | 2011-03-29T07:06:51.563 | 2011-03-29T14:41:30.803 | 2011-03-29T14:41:30.803 | 2958 | 3681 | [
"conditional-probability"
] |
8901 | 2 | null | 1564 | 1 | null | check this [wikipedia](http://en.wikipedia.org/wiki/Bayes%27_theorem#Generalizations) page under the sub-section named extensions, they do show how to derive conditional probability involving more than 2 events.
| null | CC BY-SA 4.0 | null | 2011-03-29T08:44:46.397 | 2022-05-12T13:52:16.437 | 2022-05-12T13:52:16.437 | 256587 | 3837 | null |
8903 | 1 | null | null | 6 | 5917 | I'm computing cosine similarities between 2 vectors.
These vectors are information retrieval query and document representations respectively.
They have been computed using [tf-idf](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) weights.
Since my documents have different length, tf-idf weights are theoretically unbounded.... | Comparing cosine similarities for tf-idf vectors for documents with different length | CC BY-SA 2.5 | null | 2011-03-29T09:06:24.640 | 2012-06-17T16:23:19.430 | 2011-03-29T11:55:26.907 | 449 | 3941 | [
"text-mining",
"information-retrieval"
] |
8904 | 1 | null | null | 1 | 5301 | I need to run Newey West t statistics in SAS 9.2. I already run regression, White's test, Breusch Godfrey test and Jarque-Bera normality test.
Regression is simple. Number of observations = 522
Depended variable name: rtest 1
Independent variable name: rtest 2
Data are time series data.
I found somewhere that i should... | How to calculate Newey West t-statistic in SAS 9.2? | CC BY-SA 2.5 | null | 2011-03-29T09:09:00.380 | 2015-07-29T12:25:19.890 | 2011-03-29T11:27:01.840 | 2116 | null | [
"sas"
] |
8905 | 2 | null | 8899 | 0 | null | Using subjective probabilities adding the “information that $P(B)=1$ to the model as an equation” is no different from adding the “data that $B$ is observed to be true”. So you can use the Bayes theorem:
$P_{prior} = \begin{smallmatrix} 0.05 \cdot 0.01 & 0.01 \cdot 0.99
\\ 0.95 \cdot 0.0... | null | CC BY-SA 2.5 | null | 2011-03-29T09:23:21.043 | 2011-03-29T09:37:12.443 | 2011-03-29T09:37:12.443 | 3911 | 3911 | null |
8906 | 2 | null | 8899 | 1 | null | When you set $Pr(B)=1$ other things will change, though some can remain the same. So you have to decide what is remaining the same.
For example, in the first part, you could have worked out $Pr(A|B)$, $Pr( \lnot A|B)$, $Pr(A| \lnot B) $ and $Pr( \lnot A| \lnot B)$. So $Pr(A|B) = \frac{Pr(B|A)Pr(A)}{Pr(B)} = \frac{... | null | CC BY-SA 2.5 | null | 2011-03-29T09:39:14.657 | 2011-03-29T09:39:14.657 | null | null | 2958 | null |
8907 | 1 | 8910 | null | 5 | 5073 | I have downloaded the Gaussian Processes for Machine Learning (GPML) package (gpml-matlab-v3.1-2010-09-27.zip) from the website,
and I can run the regression example ([demoRegression](http://www.gaussianprocess.org/gpml/code/matlab/doc/index.html)) in [Octave](http://www.gnu.org/software/octave/). It works just fine.
N... | How do I use the GPML package for multi dimensional input? | CC BY-SA 3.0 | null | 2011-03-29T09:47:18.770 | 2014-10-14T17:22:21.210 | 2013-07-24T16:41:33.833 | 12786 | 3943 | [
"regression",
"machine-learning",
"matlab",
"stochastic-processes",
"nonparametric-bayes"
] |
8908 | 1 | 8913 | null | 3 | 283 | I'm working up a taxonomy showing different methods used in pattern recognition and I'd be curious to hear about how it could be improved. The Mind Map groups different methods based on the discipline which influenced their development.
[taxonomy http://bentham.k2.t.u-tokyo.ac.jp/media/zoo.png](http://bentham.k2.t.u-to... | What is missing from this taxonomy of methods used in pattern recognition? | CC BY-SA 3.0 | null | 2011-03-29T10:09:49.400 | 2011-10-26T09:55:35.580 | 2011-10-26T09:55:35.580 | 183 | 2624 | [
"algorithms"
] |
8909 | 1 | 8989 | null | 6 | 1163 | I am designing a data capture method for a client for inplay sporting events and he wants to record the odds movements for later analysis in Excel once every half second. I want to get this right so that it's easy to use the data down the line for analysis in other packages.
A bit more background and assumptions.
- Ea... | Data collection and storage for time series analysis | CC BY-SA 2.5 | null | 2011-03-29T10:26:51.437 | 2011-03-31T00:26:24.340 | null | null | null | [
"time-series",
"dataset"
] |
8910 | 2 | null | 8907 | 8 | null | Here is a more minimal example of a 2-d regression problem (I haven't got octave, only matlab, but hopefully the difference won't matter). meanfunc and covfunc should be happy with any number of inputs, provided that the covariance function doesn't have a hyper-parameter per inpit feature (as e.g. `covSEiso` does). H... | null | CC BY-SA 3.0 | null | 2011-03-29T10:28:10.890 | 2012-02-10T21:49:27.770 | 2012-02-10T21:49:27.770 | 9119 | 887 | null |
8911 | 1 | null | null | 4 | 7501 | What free tool can I use to do simple Monte Carlo simulations on OS X?
| What free tool can I use to do simple Monte Carlo simulations on OS X? | CC BY-SA 2.5 | null | 2011-03-29T12:00:38.800 | 2021-12-05T02:32:24.547 | null | null | 1901 | [
"monte-carlo"
] |
8913 | 2 | null | 8908 | 1 | null | Maximum entropy Markov models could go next to hidden Markov models.
| null | CC BY-SA 2.5 | null | 2011-03-29T12:45:45.907 | 2011-03-29T12:45:45.907 | null | null | 3874 | null |
8914 | 2 | null | 8911 | 9 | null | [](http://www.r-project.org/)
What is a probability that a sum of a 3 highest results from 5 throws of a die is divisible by seven?
```
> mean(replicate(1e5,sum(sort(sample(1:6,5,replace=T))[3:5])%%7==0))
[1] 0.16068
> mean(replicate(1e5,sum(sort(sample(1:6,5,replace=T))[3:5])%%7==0))
[1] 0.16032
```
Circa 16%.
| null | CC BY-SA 3.0 | null | 2011-03-29T12:46:58.267 | 2014-09-15T05:43:28.060 | 2014-09-15T05:43:28.060 | 44269 | null | null |
8915 | 2 | null | 8911 | 3 | null | My favourite platforms are
- PyMC and
- OpenBUGS
PyMC runs on OS X out of the box, OpenBUGS is originally for windows, but according to [this](http://www.openbugs.info/w/Downloads) it can be run using Wine.
| null | CC BY-SA 2.5 | null | 2011-03-29T12:56:26.060 | 2011-03-29T12:56:26.060 | null | null | 3911 | null |
8916 | 1 | null | null | 5 | 62 | I am working on joint and conditional density trees for approximating clique potentials in Bayesian Belief Networks. A brief introduction to topic is available from [this paper](http://www.autonlab.org/autonweb/14653.html) in case you'd like to get a better description what I'm talking about.
I am looking for an implem... | Are there any available implementations of density or conditional density tree learning? | CC BY-SA 3.0 | null | 2011-03-29T12:58:21.373 | 2016-12-09T08:36:42.547 | 2016-12-09T08:36:42.547 | 113090 | 3280 | [
"bayesian",
"multivariate-analysis",
"cart",
"approximation",
"bayesian-network"
] |
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