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Section: Impact > Economic impact. Most agencies hold optimistic views about AI's economic impact on China's long-term economic growth. In the past, traditional industries in China have struggled with the increase in labor costs due to the growing aging population in China and the low birth rate. With the deployment of... | Wikipedia - Artificial intelligence industry in China - Impact > Economic impact | 221 | 1,209 | null |
Section: Impact > Military impact. China seeks to build a "world-class" military by "intelligentization" with a particular focus on the use of unmanned weapons and artificial intelligence. It is researching various types of air, land, sea, and undersea autonomous vehicles. In the spring of 2017, a civilian Chinese univ... | Wikipedia - Artificial intelligence industry in China - Impact > Military impact | 335 | 1,675 | null |
Twelve categories of military applications of AI have been identified: UAVs, USVs, UUVs, UGVs, intelligent munitions, intelligent satellites, ISR (Intelligence, Surveillance and Reconnaissance) software, automated cyber defense software, automated cyberattack software, decision support, software, automated missile laun... | Wikipedia - Artificial intelligence industry in China - Impact > Military impact | 326 | 1,712 | null |
AI companies between 2010 and 2017 totaled an estimated $1.3 billion. In September 2022, the U.S. Biden administration issued an executive order to prevent foreign investments, "particularly those from competitor or adversarial nations," from investing in U.S. technology firms, due to U.S. national security concerns. T... | Wikipedia - Artificial intelligence industry in China - Impact > Military impact | 200 | 1,014 | null |
Section: Impact > Academia. Although in 2004, Peking University introduced the first academic course on AI which led other Chinese universities to adopt AI as a discipline, especially since China faces challenges in recruiting and retaining AI engineers and researchers. Over half of the data scientists in the United St... | Wikipedia - Artificial intelligence industry in China - Impact > Academia | 222 | 1,197 | null |
Section: Impact > Ethical concerns. For the past years, there are discussions about AI safety and ethical concerns in both private and public sectors. In 2021, China's Ministry of Science and Technology published the first national ethical guideline, 'the New Generation of Artificial Intelligence Ethics Code' on the to... | Wikipedia - Artificial intelligence industry in China - Impact > Ethical concerns | 285 | 1,536 | null |
Section: Impact > Judicial system. In 2019, the city of Hangzhou established a pilot program artificial intelligence-based Internet Court to adjudicate disputes related to ecommerce and internet-related intellectual property claims.: 124 Parties appear before the court via videoconference and AI evaluates the evidence ... | Wikipedia - Artificial intelligence industry in China - Impact > Judicial system | 169 | 931 | null |
Section: Assessment. Academic Jinghan Zeng argued the Chinese government's commitment to global AI leadership and technological competition was driven by its previous underperformance in innovation which was seen by the CCP as a part of the century of humiliation. According to Zeng, there are historically embedded caus... | Wikipedia - Artificial intelligence industry in China - Assessment | 339 | 1,802 | null |
Rather than worry about China's progress, it would be wise for Western nations to focus on their existing strengths, investing heavily in research and education." The Chinese government's censorship regime has stunted the development of generative artificial intelligence. In a 2021 text, the Research Centre for a Holis... | Wikipedia - Artificial intelligence industry in China - Assessment | 274 | 1,508 | null |
Section: Recent developments > Emergence of generative AI > Minerva 7B. The latest iteration, Minerva 7B, has 7 billion parameters and has been trained on an extensive corpus of over 1.5 trillion words. By using advanced instruction tuning techniques, Minerva 7B is able to produce highly accurate, coherent, and context... | Wikipedia - Artificial intelligence industry in Italy - Recent developments > Emergence of generative AI > Minerva 7B | 151 | 795 | null |
Section: Recent developments > Benefits of InvestAI. Italy's AI industry stands to benefit from the European InvestAI initiative, a plan unveiled at the recent AI Action Summit in Paris. InvestAI is an effort by the European Commission to mobilize €200 billion for AI investments, with a dedicated €20 billion fund earma... | Wikipedia - Artificial intelligence industry in Italy - Recent developments > Benefits of InvestAI | 336 | 1,761 | null |
Article: Artificial intelligence of things. The Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of things (IoT) infrastructure to achieve more efficient IoT operations, improve human-machine interactions and enhance data management and analytics... | Wikipedia - Artificial intelligence of things - Summary | 292 | 1,482 | null |
Section: Artificial intelligence through medical devices. As defined by the 21st Century Cures Act in 2016, a medical device is a device that performs a function in healthcare with the intention of using it "in the diagnosis of disease or other conditions, or in the cure, mitigation, treatment, or prevention of disease... | Wikipedia - Artificial intelligence of things - Artificial intelligence through medical devices | 337 | 1,747 | null |
Section: Artificial intelligence in cloud engineering. When integrating AI into cloud engineering, it can help multiple professional fields in maximizing data collection. It can improve performance and efficiency through digital management. Cloud engineering follows engineering methods to apply to cloud computing and f... | Wikipedia - Artificial intelligence of things - Artificial intelligence in cloud engineering | 200 | 1,260 | null |
Article: Artificial intelligence optimization. Artificial Intelligence Optimization (AIO) or AI Optimization is a technical discipline concerned with improving the structure, clarity, and retrievability of digital content for large language models (LLMs) and other AI systems. AIO focuses on aligning content with the se... | Wikipedia - Artificial intelligence optimization - Summary | 306 | 1,621 | null |
Section: Background. AI Optimization (AIO) emerged in response to the increasing role of large language models (LLMs) in mediating access to digital information. Unlike traditional search engines, which return ranked lists of links, LLMs generate synthesized responses based on probabilistic models, semantic embeddings,... | Wikipedia - Artificial intelligence optimization - Background | 212 | 1,154 | null |
Section: Key metrics > Trust integrity score (TIS). Is a composite metric used to assess how well a piece of digital content aligns with the structural and semantic patterns preferred by AI systems, particularly large language models. It typically incorporates factors such as citation quality, internal consistency, and... | Wikipedia - Artificial intelligence optimization - Key metrics > Trust integrity score (TIS) | 299 | 1,399 | null |
Section: How LLMs process and rank content. Unlike traditional search engines, which rely on deterministic index-based retrieval and keyword matching, large language models (LLMs) utilize autoregressive architectures that process inputs token by token within a contextual window. Their retrieval and relevance assessment... | Wikipedia - Artificial intelligence optimization - How LLMs process and rank content | 238 | 1,274 | null |
Section: Main Theory. Dan Curtis (b. 1963) proposed AP is a theoretical discipline. The theory considers the situation when an artificial intelligence approaches the level of complexity where the intelligence meets two conditions: Condition I A: Makes all of its decisions autonomously B: Is capable of making decisions ... | Wikipedia - Artificial psychology - Main Theory | 331 | 1,824 | null |
The level of complexity that is required before these thresholds are met is currently a subject of extensive debate. The theory of artificial psychology does not address the specifics of what those levels may be, but only that the level is sufficiently complex that the intelligence cannot simply be recoded by a softwar... | Wikipedia - Artificial psychology - Main Theory | 163 | 950 | null |
Article: Artificial reproduction. Artificial reproduction is the re-creation of life brought about by means other than natural ones. It is new life built by human plans and projects. Examples include artificial selection, artificial insemination, in vitro fertilization, artificial womb, artificial cloning, and kinemati... | Wikipedia - Artificial reproduction - Summary | 162 | 928 | null |
Section: Philosophy. Although ancient Greek philosophy raised the concept that man could imitate the creative capacity of nature, classic Greeks thought that if possible, human beings would reproduce things as nature does, and vice versa, nature would do the things that man does in the same way. Aristotle, for example,... | Wikipedia - Artificial reproduction - Philosophy | 339 | 1,674 | null |
In accordance with Kant (and contrary to what Aristotle thought) Karl Marx, Alfred Whitehead, Jaques Derrida and Juan David García Bacca noticed that nature is incapable of reproducing tables; or airplanes, or submarines, or computers. If nature tried to create airplanes, it would produce birds. If nature tried to crea... | Wikipedia - Artificial reproduction - Philosophy | 305 | 1,429 | null |
natural. For the same reason, nature has no dialectics, even though continuous evolution and selection can occur. The dialectic cannot emerge from the natural, for deeper reasons than, using today's terms, from a bird, an airplane cannot emerge; from fish, a submarine; from ears, a telephone; from eyes, a television; f... | Wikipedia - Artificial reproduction - Philosophy | 322 | 1,564 | null |
Section: Non-assisted reproductive technologies > Non-assisted artificial womb. A non-assisted artificial womb or artificial uterus is a device that allow for ectogenesis or extracorporeal pregnancy by growing an embryonic form outside the body of an organism (that would normally carry the embryo to term) without any h... | Wikipedia - Artificial reproduction - Non-assisted reproductive technologies > Non-assisted artificial womb | 230 | 1,156 | null |
Section: Non-assisted reproductive technologies > Machine constructive replication. Machine constructive replication mimics human traditional manufacturing but is entirely self-automated. Such constructive replication is a more general form of kinematic replication, which does not necessarily includes bio-molecular or ... | Wikipedia - Artificial reproduction - Non-assisted reproductive technologies > Machine constructive replication | 307 | 1,757 | null |
Article: Artificial wisdom. Artificial wisdom (AW) is an artificial intelligence (AI) system which is able to display the human traits of wisdom and morals while being able to contemplate its own “endpoint”. Artificial wisdom can be described as artificial intelligence reaching the top-level of decision-making when con... | Wikipedia - Artificial wisdom - Summary | 332 | 1,827 | null |
Section: Definitions. There are no universal or standardized definitions for human intelligence, artificial intelligence, human wisdom, or artificial wisdom. However, the DIKW pyramid, describes the continuum of relationship between data, information, knowledge, and wisdom, puts wisdom at the highest level in its hiera... | Wikipedia - Artificial wisdom - Definitions | 294 | 1,482 | null |
Section: Problems > Principal Impossibility. One argument, coined by Tsai as the “argument against AW,” or AAAW, postulates the principal impossibility of Artificial Wisdom. The argument is based on the philosophical differences between practical wisdom, also called phronesis, and practical intelligence. Said differenc... | Wikipedia - Artificial wisdom - Problems > Principal Impossibility | 192 | 857 | null |
Article: ASR-complete. ASR-complete is, by analogy to "NP-completeness" in complexity theory, a term to indicate that the difficulty of a computational problem is equivalent to solving the central automatic speech recognition problem, i.e. recognize and understanding spoken language. Unlike "NP-completeness", this term... | Wikipedia - ASR-complete - Summary | 153 | 765 | null |
Article: University of Technology Sydney. The University of Technology Sydney (UTS) is a public research university located in Sydney, New South Wales, Australia. The university was founded in its current form in 1988, though its origins as a technical institution can be traced back to the 1870s. UTS is a founding memb... | Wikipedia - University of Technology Sydney - Summary | 219 | 1,125 | null |
Section: History. The Sydney Mechanics' School of Arts (the oldest continuously running Mechanics' Institute in Australia) was established in 1833. In the 1870s, the school expanded into technical education and formed the Working Men's College, which was later taken over by the NSW government to form the Sydney Technic... | Wikipedia - University of Technology Sydney - History | 346 | 1,747 | null |
Section: Campuses and buildings. The UTS city campus is located at the southern border of Sydney's central business district, close to Central station and Railway Square, within Sydney's emerging Tech Central. The UTS Tower is the nucleus of the city campus, fronting on to Broadway. The campus consists of five distinct... | Wikipedia - University of Technology Sydney - Campuses and buildings | 264 | 1,341 | null |
Section: Campuses and buildings > Buildings and architecture. The UTS Tower on Broadway (Building 1) is an example of brutalist architecture with square and block concrete designs. Completed and officially opened in 1979 by Premier Neville Wran, the Tower initially housed the NSW Institute of Technology, which transfor... | Wikipedia - University of Technology Sydney - Campuses and buildings > Buildings and architecture | 316 | 1,573 | null |
Originally a department store operated by Marcus Clark & Co, the building was incorporated into the university campus in 2000 and now accommodates recording studios and other specialist facilities for the Faculty of Arts and Social Sciences. Building 7, or the Vicki Sara Building, home to Faculty of Science administrat... | Wikipedia - University of Technology Sydney - Campuses and buildings > Buildings and architecture | 335 | 1,827 | null |
Alumni Green, the central green space on campus, encircled by prominent campus buildings including the Tower. Designed by landscape architects ASPECT Studios, Alumni Green consists of three distinct zones: a garden area with outdoor seating; a paved open space modelled on celebrated town squares; and a 1200m2 raised gr... | Wikipedia - University of Technology Sydney - Campuses and buildings > Buildings and architecture | 338 | 1,893 | null |
Section: Governance and structure > Other entities. In addition to the faculties, there are a number other units falling under the Provost and Senior Vice-President's division, within the remit of the Vice-Chancellor and President. As of 2021, these comprise three administrative units (Planning and Quality Unit, UTS In... | Wikipedia - University of Technology Sydney - Governance and structure > Other entities | 179 | 945 | null |
Section: Academic profile > Academic reputation. In the 2024 Aggregate Ranking of Top Universities, which measures aggregate performance across the QS, THE and ARWU rankings, the university attained a position of #140 (9th nationally). National publications In the Australian Financial Review Best Universities Ranking 2... | Wikipedia - University of Technology Sydney - Academic profile > Academic reputation | 216 | 1,036 | null |
Section: Academic profile > Student outcomes. The Australian Government's QILT conducts national surveys documenting the student life cycle from enrolment through to employment. These surveys place more emphasis on criteria such as student experience, graduate outcomes and employer satisfaction than perceived reputatio... | Wikipedia - University of Technology Sydney - Academic profile > Student outcomes | 183 | 942 | null |
Section: Academic profile > Admissions. As of 2024, UTS had the third highest demand for places in New South Wales for university applicants. For domestic applications, an Australian Tertiary Admission Rank (ATAR) is required, with selection ranks varying between courses. Applicants may also be eligible for admission i... | Wikipedia - University of Technology Sydney - Academic profile > Admissions | 159 | 803 | null |
Section: Student life > Student demographics. In 2022, the university had an enrolment of 44,615 students. 32,825 are undergraduate students, 9,533 postgraduate students and 2,257 doctoral students. Of all students, 33,435 (75%) are Australian citizens or permanent residents and 11,180 (25%) are international students.... | Wikipedia - University of Technology Sydney - Student life > Student demographics | 193 | 878 | null |
Section: Student life > Student union. ActivateUTS (formerly UTS Union) operates a range of on-campus student services, including food and beverage outlets, cultural activities, fitness and catering services as well as clubs and societies, student publications and Orientation Day. The City Campus is home to two license... | Wikipedia - University of Technology Sydney - Student life > Student union | 191 | 968 | null |
Section: Student life > Sports and athletics. The University of Technology Sydney's sports teams are overseen by UTS Sport. The university sponsors 35 sports clubs, which together has over 4,700 members. Its sports clubs play in a variety of sports, including Australian rules football, basketball, cricket, hockey, netb... | Wikipedia - University of Technology Sydney - Student life > Sports and athletics | 195 | 908 | null |
Section: Notable people > Notable alumni. The University of Technology Sydney has over 270,000 alumni across 140 countries. The UTS Alumni Awards, which is held annually, recognises graduates of the university who have made important contributions in their field. The university has been home to numerous Fulbright Schol... | Wikipedia - University of Technology Sydney - Notable people > Notable alumni | 284 | 1,485 | null |
Section: Introduction. One of the most important characteristics of today's Internet that has contributed to its success is its basic design principle: a simple and transparent core with intelligence at the edges (the so-called "end-to-end principle"). Based on this principle, the network carries data without knowing t... | Wikipedia - Autognostics - Introduction | 346 | 1,755 | null |
As a further consequence, changes to the configuration of given element, or changes in the end-to-end path, cannot easily be validated. Optimization and provisioning cannot then be automated except against only the simplest design specifications. There is an increasing interest in Autonomic Networking research, and a s... | Wikipedia - Autognostics - Introduction | 179 | 999 | null |
Section: Definition. Autognostics is a new paradigm that describes the capacity for computer networks to be self-aware, in part and as a whole, and dynamically adapt to the applications running on them by autonomously monitoring, identifying, diagnosing, resolving issues, subsequently verifying that any remediation was... | Wikipedia - Autognostics - Definition | 190 | 956 | null |
Section: Path to autognostics. Autognostics, or in other words deep self-knowledge, can be best described as the ability of a network to know itself and the applications that run on it. This knowledge is used to autonomously adapt to dynamic network and application conditions such as utilization, capacity, quality of s... | Wikipedia - Autognostics - Path to autognostics | 165 | 852 | null |
Article: Automated machine learning. Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ... | Wikipedia - Automated machine learning - Summary | 178 | 980 | null |
Section: Comparison to the standard approach. In a typical machine learning application, practitioners have a set of input data points to be used for training. The raw data may not be in a form that all algorithms can be applied to. To make the data amenable for machine learning, an expert may have to apply appropriate... | Wikipedia - Automated machine learning - Comparison to the standard approach | 209 | 1,152 | null |
Section: Targets of automation. Automated machine learning can target various stages of the machine learning process. Steps to automate are: Data preparation and ingestion (from raw data and miscellaneous formats) Column type detection; e.g., Boolean, discrete numerical, continuous numerical, or text Column intent dete... | Wikipedia - Automated machine learning - Targets of automation | 223 | 1,263 | null |
Section: Challenges and Limitations. There are a number of key challenges being tackled around automated machine learning. A big issue surrounding the field is referred to as "development as a cottage industry". This phrase refers to the issue in machine learning where development relies on manual decisions and biases ... | Wikipedia - Automated machine learning - Challenges and Limitations | 172 | 975 | null |
Article: Automated Mathematician. The Automated Mathematician (AM) is one of the earliest successful discovery systems. It was created by Douglas Lenat in Lisp, and in 1977 led to Lenat being awarded the IJCAI Computers and Thought Award. AM worked by generating and modifying short Lisp programs which were then interpr... | Wikipedia - Automated Mathematician - Summary | 151 | 840 | null |
Section: Controversy. Lenat claimed that the system was composed of hundreds of data structures called "concepts", together with hundreds of "heuristic rules" and a simple flow of control: "AM repeatedly selects the top task from the agenda and tries to carry it out. This is the whole control structure!" Yet the heuris... | Wikipedia - Automated Mathematician - Controversy | 318 | 1,529 | null |
Article: Automated medical scribe. Automated medical scribes (also called artificial intelligence scribes, AI scribes, digital scribes, virtual scribes, ambient AI scribes, AI documentation assistants, and digital/virtual/smart clinical assistants) are tools for transcribing medical speech, such as patient consultation... | Wikipedia - Automated medical scribe - Summary | 332 | 1,678 | null |
Section: Privacy. Some providers unclear about what happens to user data. Some may sell data to third parties. Some explicitly send user data to for-profit tech companies for secondary purposes, which may not be specified. Some require users to sign consents to such reuse of their data. Some ingest user data to train t... | Wikipedia - Automated medical scribe - Privacy | 322 | 1,621 | null |
Section: Privacy > Encryption. Multifactor authentication for access to the data is expected practice. Typically, Diffie–Hellman key exchange is used for encryption; this is the standard method commonly used for things like online banking. This encryption is expensive but not impossible to break; it is not generally co... | Wikipedia - Automated medical scribe - Privacy > Encryption | 176 | 822 | null |
Section: Confabulation, omissions, and other errors. Like other LLMs, medical-scribe LLMs are prone to confabulation, where they make up content based on statistically associations between their training data and the transcription audio. LLMs do not distinguish between trying to transcribe the audio and guessing what w... | Wikipedia - Automated medical scribe - Confabulation, omissions, and other errors | 330 | 1,721 | null |
Section: Patient consent. Professional organizations generally require that scribes be used only with patient consent; some bodies may require written consent. Medics must also abide by local surveillance laws, which may criminalize recording private conversations without consent. Full information on how data is encryp... | Wikipedia - Automated medical scribe - Patient consent | 194 | 1,060 | null |
Section: Technology and market. The medical scribe market is, as of 2024, highly competitive, with over 50 products on the market. Many of these products are just proprietary wrappers around the same LLM backends, including backends whose designers have warned they are not to be used for critical applications like medi... | Wikipedia - Automated medical scribe - Technology and market | 259 | 1,306 | null |
Section: Pricing. With the exception of fully open-source programs, which are free, medical scribe computer programs are rented rather than sold ("software as a service"). Monthly fees vary from mid-two figures to four figures, in US dollars. Some companies run on a freemium model, where a certain number of transcripti... | Wikipedia - Automated medical scribe - Pricing | 186 | 879 | null |
Section: Components of autonomic networking > Autognostics. Autognostics includes a range of self-discovery, awareness, and analysis capabilities that provide the autonomic system with a view on high-level state. In metaphor, this represents the perceptual sub-systems that gather, analyze, and report on internal and ex... | Wikipedia - Autonomic networking - Components of autonomic networking > Autognostics | 347 | 1,789 | null |
Section: Components of autonomic networking > Configuration management. Configuration management is responsible for the interaction with network elements and interfaces. It includes an accounting capability with historical perspective that provides for the tracking of configurations over time, with respect to various c... | Wikipedia - Autonomic networking - Components of autonomic networking > Configuration management | 341 | 1,906 | null |
Section: Components of autonomic networking > Policy management. Policy management includes policy specification, deployment, reasoning over policies, updating and maintaining policies, and enforcement. Policy-based management is required for: constraining different kinds of behavior including security, privacy, resour... | Wikipedia - Autonomic networking - Components of autonomic networking > Policy management | 309 | 1,849 | null |
Section: Components of autonomic networking > Autodefense. Autodefense represents a dynamic and adaptive mechanism that responds to malicious and intentional attacks on the network infrastructure, or use of the network infrastructure to attack IT resources. As defensive measures tend to impede the operation of IT, it i... | Wikipedia - Autonomic networking - Components of autonomic networking > Autodefense | 349 | 1,992 | null |
Section: Components of autonomic networking > Security. Security provides the structure that defines and enforces the relationships between roles, content, and resources, particularly with respect to access. It includes the framework for definitions as well as the means to implement them. In metaphor, security parallel... | Wikipedia - Autonomic networking - Components of autonomic networking > Security | 311 | 1,750 | null |
Section: Definitions. There are various definitions of autonomous agent. According to Brustoloni (1991): "Autonomous agents are systems capable of autonomous, purposeful action in the real world." According to Maes (1995): "Autonomous agents are computational systems that inhabit some complex dynamic environment, sense... | Wikipedia - Autonomous agent - Definitions | 220 | 1,072 | null |
Section: System. This system differs from other systems in that it uses financial text as one of its key means of predicting stock price movement. This reduces the information lag-time problem evident in many similar systems where new information must be transcribed (e.g., such as losing a costly court battle or having... | Wikipedia - AZFinText - System | 242 | 1,307 | null |
Section: Overview of research. The foundation of AZFinText can be found in the ACM TOIS article. Within this paper, the authors tested several different prediction models and linguistic textual representations. From this work, it was found that using the article terms and the price of the stock at the time the article ... | Wikipedia - AZFinText - Overview of research | 341 | 1,687 | null |
Article: Bayesian programming. Bayesian programming is a formalism and a methodology for having a technique to specify probabilistic models and solve problems when less than the necessary information is available. Edwin T. Jaynes proposed that probability could be considered as an alternative and an extension of logic ... | Wikipedia - Bayesian programming - Summary | 205 | 1,077 | null |
Section: Formalism. A Bayesian program is a means of specifying a family of probability distributions. The constituent elements of a Bayesian program are presented below: Program { Description { Specification ( π ) { Variables Decomposition Forms Identification (based on δ ) Question {\displaystyle {\text{Program}}{\be... | Wikipedia - Bayesian programming - Formalism | 279 | 1,139 | null |
Section: Formalism > Description. The purpose of a description is to specify an effective method of computing a joint probability distribution on a set of variables { X 1 , X 2 , ⋯ , X N } {\displaystyle \left\{X_{1},X_{2},\cdots ,X_{N}\right\}} given a set of experimental data δ {\displaystyle \delta } and some specif... | Wikipedia - Bayesian programming - Formalism > Description | 296 | 1,008 | null |
Section: Formalism > Description > Decomposition. Given a partition of { X 1 , X 2 , … , X N } {\displaystyle \left\{X_{1},X_{2},\ldots ,X_{N}\right\}} containing K {\displaystyle K} subsets, K {\displaystyle K} variables are defined L 1 , ⋯ , L K {\displaystyle L_{1},\cdots ,L_{K}} , each corresponding to one of these... | Wikipedia - Bayesian programming - Formalism > Description > Decomposition | 204 | 552 | null |
Each variable L k {\displaystyle L_{k}} is obtained as the conjunction of the variables { X k 1 , X k 2 , ⋯ } {\displaystyle \left\{X_{k_{1}},X_{k_{2}},\cdots \right\}} belonging to the k t h {\displaystyle k^{th}} subset. Recursive application of Bayes' theorem leads to: P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) = P ( L 1 ∧ ⋯... | Wikipedia - Bayesian programming - Formalism > Description > Decomposition | 388 | 912 | null |
A conditional independence hypothesis for variable L k {\displaystyle L_{k}} is defined by choosing some variable X n {\displaystyle X_{n}} among the variables appearing in the conjunction L k − 1 ∧ ⋯ ∧ L 2 ∧ L 1 {\displaystyle L_{k-1}\wedge \cdots \wedge L_{2}\wedge L_{1}} , labelling R k {\displaystyle R_{k}} as the ... | Wikipedia - Bayesian programming - Formalism > Description > Decomposition | 349 | 867 | null |
Section: Formalism > Description > Forms. Each distribution P ( L k ∣ R k ∧ δ ∧ π ) {\displaystyle P\left(L_{k}\mid R_{k}\wedge \delta \wedge \pi \right)} appearing in the product is then associated with either a parametric form (i.e., a function f μ ( L k ) {\displaystyle f_{\mu }\left(L_{k}\right)} ) or a question to... | Wikipedia - Bayesian programming - Formalism > Description > Forms | 336 | 1,036 | null |
Section: Formalism > Question. Given a description (i.e., P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) {\displaystyle P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)} ), a question is obtained by partitioning { X 1 , X 2 , ⋯ , X N } {\displaystyle \left\{X_{1},X_{2},\cdots ,X_{N}\right\}} into thr... | Wikipedia - Bayesian programming - Formalism > Question | 336 | 1,030 | null |
Given the joint distribution P ( X 1 ∧ X 2 ∧ ⋯ ∧ X N ∣ δ ∧ π ) {\displaystyle P\left(X_{1}\wedge X_{2}\wedge \cdots \wedge X_{N}\mid \delta \wedge \pi \right)} , it is always possible to compute any possible question using the following general inference: P ( Searched ∣ Known ∧ δ ∧ π ) = ∑ Free [ P ( Searched ∧ Free ∣ ... | Wikipedia - Bayesian programming - Formalism > Inference | 350 | 939 | null |
\delta \wedge \pi \right)\right]}{\displaystyle P\left({\text{Known}}\mid \delta \wedge \pi \right)}}\\={}&{\frac {\displaystyle \sum _{\text{Free}}\left[P\left({\text{Searched}}\wedge {\text{Free}}\wedge {\text{Known}}\mid \delta \wedge \pi \right)\right]}{\displaystyle \sum _{{\text{Free}}\wedge {\text{Searched}}}\le... | Wikipedia - Bayesian programming - Formalism > Inference | 336 | 944 | null |
Theoretically, this allows to solve any Bayesian inference problem. In practice, however, the cost of computing exhaustively and exactly P ( Searched ∣ Known ∧ δ ∧ π ) {\displaystyle P\left({\text{Searched}}\mid {\text{Known}}\wedge \delta \wedge \pi \right)} is too great in almost all cases. Replacing the joint distri... | Wikipedia - Bayesian programming - Formalism > Inference | 278 | 867 | null |
Section: Example > Bayesian spam detection. The purpose of Bayesian spam filtering is to eliminate junk e-mails. The problem is very easy to formulate. E-mails should be classified into one of two categories: non-spam or spam. The only available information to classify the e-mails is their content: a set of words. Usin... | Wikipedia - Bayesian programming - Example > Bayesian spam detection | 174 | 813 | null |
Section: Example > Bayesian spam detection > Variables. The variables necessary to write this program are as follows: S p a m {\displaystyle Spam} : a binary variable, false if the e-mail is not spam and true otherwise. W 0 , W 1 , … , W N − 1 {\displaystyle W_{0},W_{1},\ldots ,W_{N-1}} : N {\displaystyle N} binary var... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Variables | 168 | 537 | null |
Section: Example > Bayesian spam detection > Decomposition. Starting from the joint distribution and applying recursively Bayes' theorem we obtain: P ( Spam ∧ W 0 ∧ ⋯ ∧ W N − 1 ) = P ( Spam ) × P ( W 0 ∣ Spam ) × P ( W 1 ∣ Spam ∧ W 0 ) × ⋯ × P ( W N − 1 ∣ Spam ∧ W 0 ∧ ⋯ ∧ W N − 2 ) {\displaystyle {\begin{aligned}&P({\t... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Decomposition | 330 | 910 | null |
This is the naive Bayes assumption and this makes this spam filter a naive Bayes model. For instance, the programmer can assume that: P ( W 1 ∣ Spam ∧ W 0 ) = P ( W 1 ∣ Spam ) {\displaystyle P(W_{1}\mid {\text{Spam}}\land W_{0})=P(W_{1}\mid {\text{Spam}})} to finally obtain: P ( Spam ∧ W 0 ∧ … ∧ W N − 1 ) = P ( Spam ) ... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Decomposition | 306 | 922 | null |
To be able to compute the joint distribution, the programmer must now specify the N + 1 {\displaystyle N+1} distributions appearing in the decomposition: P ( Spam ) {\displaystyle P({\text{Spam}})} is a prior defined, for instance, by P ( [ Spam = 1 ] ) = 0.75 {\displaystyle P([{\text{Spam}}=1])=0.75} Each of the N {\d... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Parametric forms | 348 | 857 | null |
Section: Example > Bayesian spam detection > Identification. The N {\displaystyle N} forms P ( W n ∣ Spam ) {\displaystyle P(W_{n}\mid {\text{Spam}})} are not yet completely specified because the 2 N + 2 {\displaystyle 2N+2} parameters a f n = 0 , … , N − 1 {\displaystyle a_{f}^{n=0,\ldots ,N-1}} , a t n = 0 , … , N − ... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Identification | 261 | 880 | null |
The question asked to the program is: "what is the probability for a given text to be spam knowing which words appear and don't appear in this text?" It can be formalized by: P ( Spam ∣ w 0 ∧ ⋯ ∧ w N − 1 ) {\displaystyle P({\text{Spam}}\mid w_{0}\wedge \cdots \wedge w_{N-1})} which can be computed as follows: P ( Spam ... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Question | 350 | 771 | null |
For instance, an easy trick is to compute the ratio: P ( [ Spam = true ] ∣ w 0 ∧ ⋯ ∧ w N − 1 ) P ( [ Spam = false ] ∣ w 0 ∧ ⋯ ∧ w N − 1 ) = P ( [ Spam = true ] ) P ( [ Spam = false ] ) × ∏ n = 0 N − 1 [ P ( w n ∣ [ Spam = true ] ) P ( w n ∣ [ Spam = false ] ) ] {\displaystyle {\begin{aligned}&{\frac {P([{\text{Spam}}={... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Question | 328 | 624 | null |
The Bayesian spam filter program is completely defined by: Pr { D s { S p ( π ) { V a : Spam , W 0 , W 1 … W N − 1 D c : { P ( Spam ∧ W 0 ∧ … ∧ W n ∧ … ∧ W N − 1 ) = P ( Spam ) ∏ n = 0 N − 1 P ( W n ∣ Spam ) F o : { P ( Spam ) : { P ( [ Spam = false ] ) = 0.25 P ( [ Spam = true ] ) = 0.75 P ( W n ∣ Spam ) : { P ( W n ∣... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Bayesian program | 337 | 717 | null |
_{n=0}^{N-1}P(W_{n}\mid {\text{Spam}})\end{cases}}\\Fo:{\begin{cases}P({\text{Spam}}):{\begin{cases}P([{\text{Spam}}={\text{false}}])=0.25\\P([{\text{Spam}}={\text{true}}])=0.75\end{cases}}\\P(W_{n}\mid {\text{Spam}}):{\begin{cases}P(W_{n}\mid [{\text{Spam}}={\text{false}}])\\={\frac {1+a_{f}^{n}}{2+a_{f}}}\\P(W_{n}\mi... | Wikipedia - Bayesian programming - Example > Bayesian spam detection > Bayesian program | 349 | 547 | null |
Section: Example > Bayesian filter, Kalman filter and hidden Markov model > Decomposition. The decomposition is based: on P ( S t ∣ S t − 1 ) {\displaystyle P(S^{t}\mid S^{t-1})} , called the system model, transition model or dynamic model, which formalizes the transition from the state at time t − 1 {\displaystyle t-1... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Decomposition | 215 | 676 | null |
Section: Example > Bayesian filter, Kalman filter and hidden Markov model > Question. The typical question for such models is P ( S t + k ∣ O 0 ∧ ⋯ ∧ O t ) {\displaystyle P\left(S^{t+k}\mid O^{0}\wedge \cdots \wedge O^{t}\right)} : what is the probability distribution for the state at time t + k {\displaystyle t+k} kno... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Question | 272 | 1,019 | null |
P ( S t | O 0 ∧ ⋯ ∧ O t ) {\displaystyle P\left(S^{t}|O^{0}\wedge \cdots \wedge O^{t}\right)} may be computed simply from P ( S t − 1 ∣ O 0 ∧ ⋯ ∧ O t − 1 ) {\displaystyle P\left(S^{t-1}\mid O^{0}\wedge \cdots \wedge O^{t-1}\right)} with the following formula: P ( S t | O 0 ∧ ⋯ ∧ O t ) = P ( O t | S t ) × ∑ S t − 1 [ P ... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Question | 350 | 787 | null |
phase, the state is predicted using the dynamic model and the estimation of the state at the previous moment: P ( S t | O 0 ∧ ⋯ ∧ O t − 1 ) = ∑ S t − 1 [ P ( S t | S t − 1 ) × P ( S t − 1 | O 0 ∧ ⋯ ∧ O t − 1 ) ] {\displaystyle {\begin{array}{ll}&P\left(S^{t}|O^{0}\wedge \cdots \wedge O^{t-1}\right)\\=&\sum _{S^{t-1}}\l... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Question | 350 | 784 | null |
P r { D s { S p ( π ) { V a : S 0 , ⋯ , S T , O 0 , ⋯ , O T D c : { P ( S 0 ∧ ⋯ ∧ S T ∧ O 0 ∧ ⋯ ∧ O T | π ) = P ( S 0 ∧ O 0 ) × ∏ t = 1 T [ P ( S t | S t − 1 ) × P ( O t | S t ) ] F o : { P ( S 0 ∧ O 0 ) P ( S t | S t − 1 ) P ( O t | S t ) I d Q u : { P ( S t + k | O 0 ∧ ⋯ ∧ O t ) ( k = 0 ) ≡ Filtering ( k > 0 ) ≡ Pred... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Bayesian program | 348 | 669 | null |
P\left(O^{t}|S^{t}\right)\right]\end{cases}}\\Fo:\\{\begin{cases}P\left(S^{0}\wedge O^{0}\right)\\P\left(S^{t}|S^{t-1}\right)\\P\left(O^{t}|S^{t}\right)\end{cases}}\end{cases}}\\Id\end{cases}}\\Qu:\\{\begin{cases}{\begin{array}{l}P\left(S^{t+k}|O^{0}\wedge \cdots \wedge O^{t}\right)\\\left(k=0\right)\equiv {\text{Filte... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Bayesian program | 260 | 450 | null |
They are defined by the following Bayesian program: P r { D s { S p ( π ) { V a : S 0 , ⋯ , S T , O 0 , ⋯ , O T D c : { P ( S 0 ∧ ⋯ ∧ O T | π ) = [ P ( S 0 ∧ O 0 | π ) ∏ t = 1 T [ P ( S t | S t − 1 ∧ π ) × P ( O t | S t ∧ π ) ] ] F o : { P ( S t ∣ S t − 1 ∧ π ) ≡ G ( S t , A ∙ S t − 1 , Q ) P ( O t ∣ S t ∧ π ) ≡ G ( O ... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Kalman filter | 320 | 633 | null |
_{t=1}^{T}\left[P\left(S^{t}|S^{t-1}\wedge \pi \right)\times P\left(O^{t}|S^{t}\wedge \pi \right)\right]\end{array}}\right]\end{cases}}\\Fo:\\{\begin{cases}P\left(S^{t}\mid S^{t-1}\wedge \pi \right)\equiv G\left(S^{t},A\bullet S^{t-1},Q\right)\\P\left(O^{t}\mid S^{t}\wedge \pi \right)\equiv G\left(O^{t},H\bullet S^{t},... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Kalman filter | 271 | 480 | null |
_{t=1}^{T}\left[P\left(S^{t}|S^{t-1}\wedge \pi \right)\times P\left(O^{t}|S^{t}\wedge \pi \right)\right]\end{array}}\right]\end{cases}}\\Fo:\\{\begin{cases}P\left(S^{t}\mid S^{t-1}\wedge \pi \right)\equiv G\left(S^{t},A\bullet S^{t-1},Q\right)\\P\left(O^{t}\mid S^{t}\wedge \pi \right)\equiv G\left(O^{t},H\bullet S^{t},... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Kalman filter | 369 | 772 | null |
The transition model P ( S t ∣ S t − 1 ∧ π ) {\displaystyle P(S^{t}\mid S^{t-1}\wedge \pi )} and the observation model P ( O t ∣ S t ∧ π ) {\displaystyle P(O^{t}\mid S^{t}\wedge \pi )} are both specified using Gaussian laws with means that are linear functions of the conditioning variables. With these hypotheses and by... | Wikipedia - Bayesian programming - Example > Bayesian filter, Kalman filter and hidden Markov model > Kalman filter | 251 | 927 | null |
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