Thomas Bayes
Thomas Bayes ( ; 1701 7 April 1761) was an English statistician, philosopher and Presbyterian minister who is known for formulating a specific case of the theorem that bears his name: Bayes' theorem. Bayes never published what would become his ...
(/beɪz/; c. 1701 – 1761) was an English statistician, philosopher, and
Presbyterian
Presbyterianism is a part of the Reformed tradition within Protestantism that broke from the Roman Catholic Church in Scotland by John Knox, who was a priest at St. Giles Cathedral (Church of Scotland). Presbyterian churches derive their nam ...
minister.
Bayesian () refers either to a range of concepts and approaches that relate to statistical methods based on
Bayes' theorem, or a follower of these methods.
BAYESIAN , Meaning & Definition for UK English , Lexico.com
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A number of things have been named after Thomas Bayes, including:
Bayes
*Bayes action
In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the ...
*Bayes Business School
Bayes Business School, formerly known as Cass Business School, is the business school of City, University of London, located in St Luke's, just to the north of the City of London. It was established in 1966, and it is consistently ranked as on ...
* Bayes classifier
* Bayes discriminability index
*Bayes error rate In statistical classification, Bayes error rate is the lowest possible error rate for any classifier of a random outcome (into, for example, one of two categories) and is analogous to the irreducible error.K. Tumer, K. (1996) "Estimating the Bayes e ...
* Bayes estimator
*Bayes factor
The Bayes factor is a ratio of two competing statistical models represented by their marginal likelihood, and is used to quantify the support for one model over the other. The models in questions can have a common set of parameters, such as a nul ...
* Bayes Impact
*Bayes linear statistics
Bayes linear statistics is a subjectivist statistical methodology and framework. Traditional subjective Bayesian analysis is based upon fully specified probability distributions, which are very difficult to specify at the necessary level of detail ...
* Bayes prior
* Bayes' theorem / Bayes-Price theorem -- sometimes called Bayes' rule or Bayesian updating.
*Empirical Bayes method
Empirical Bayes methods are procedures for statistical inference in which the prior probability distribution is estimated from the data. This approach stands in contrast to standard Bayesian methods, for which the prior distribution is fixed ...
*Evidence under Bayes theorem
The use of evidence under Bayes' theorem relates to the probability of finding evidence in relation to the accused, where Bayes' theorem concerns the probability of an event and its inverse. Specifically, it compares the probability of finding p ...
*Hierarchical Bayes model
Multilevel models (also known as hierarchical linear models, linear mixed-effect model, mixed models, nested data models, random coefficient, random-effects models, random parameter models, or split-plot designs) are statistical models of parame ...
* Laplace–Bayes estimator
* Naive Bayes classifier
* Random naive Bayes
Bayesian
*Approximate Bayesian computation
Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics that can be used to estimate the posterior distributions of model parameters.
In all model-based statistical inference, the like ...
* Bayesian average
*Bayesian Analysis (journal)
''Bayesian Analysis'' is an open-access peer-reviewed scientific journal covering theoretical and applied aspects of Bayesian methods. It is published by the International Society for Bayesian Analysis and is hosted at the Project Euclid web sit ...
*Bayesian approaches to brain function Bayesian approaches to brain function investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics. This term is used in behavioural sciences and n ...
* Bayesian bootstrap
*Bayesian control rule
Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit
In probability theory and machine learning, the multi-armed bandit problem (som ...
*Bayesian cognitive science
Bayesian cognitive science, also known as computational cognitive science, is an approach to cognitive science concerned with the rational analysis of cognition through the use of Bayesian inference and cognitive modeling. The term "computation ...
*Bayesian econometrics
Bayesian econometrics is a branch of econometrics which applies Bayesian principles to economic modelling. Bayesianism is based on a degree-of-belief interpretation of probability, as opposed to a relative-frequency interpretation.
The Bayesian ...
*Bayesian efficiency
Bayesian efficiency is an analog of Pareto efficiency for situations in which there is incomplete information.Palfrey, Thomas R.; Srivastava, Sanjay; Postlewaite, A. (1993) Bayesian Implementation.' Pg. 13-14. Under Pareto efficiency, an allocati ...
*Bayesian epistemology
Bayesian epistemology is a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory. One advantage of its formal method in contrast to traditional epistemology is that its concep ...
* Bayesian expected loss
*Bayesian experimental design Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It is based on Bayesian inference to interpret the observations/data acquired during the experiment. ...
*Bayesian game
In game theory, a Bayesian game is a game that models the outcome of player interactions using aspects of Bayesian probability. Bayesian games are notable because they allowed, for the first time in game theory, for the specification of the solut ...
*Bayesian hierarchical modeling
Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method.Allenby, Rossi, McCulloch (January 2005)"Hierarchical Bayes ...
* Bayesian History Matching
* Bayesian inference
*Bayesian inference in phylogeny
Bayesian inference of phylogeny combines the information in the prior and in the data likelihood to create the so-called posterior probability of trees, which is the probability that the tree is correct given the data, the prior and the likelihood ...
* Bayesian information criterion (BIC) and
* Widely applicable Bayesian information criterion (WBIC)
* Bayesian Kepler periodogram
*Bayesian Knowledge Tracing Bayesian knowledge tracing is an algorithm used in many intelligent tutoring systems to model each learner's mastery of the knowledge being tutored.
It models student knowledge in a hidden Markov model as a latent variable, updated by observing the ...
* Bayesian learning mechanisms
*Bayesian linear regression
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well ...
* Bayesian model of computational anatomy
*Bayesian model averaging
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
Unlike a statistical ensemble in statisti ...
(BMA)
* Bayesian model combination (BMC)
*Bayesian model reduction Bayesian model reduction is a method for computing the evidence and posterior over the parameters of Bayesian models that differ in their priors. A full model is fitted to data using standard approaches. Hypotheses are then tested by defining one ...
* Bayesian model selection
*Bayesian multivariate linear regression
In statistics, Bayesian multivariate linear regression is a
Bayesian approach to multivariate linear regression, i.e. linear regression where the predicted outcome is a vector of correlated random variables rather than a single scalar random v ...
* Bayesian Nash equilibrium
*Bayesian network
A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). Bay ...
* Bayesian neural network
* Bayesian operational modal analysis (BAYOMA)
* Bayesian-optimal mechanism
* Bayesian-optimal pricing
* Bayesian optimization
*Bayesian poisoning
Bayesian poisoning is a technique used by e-mail spammers to attempt to degrade the effectiveness of spam filters that rely on Bayesian spam filtering. Bayesian filtering relies on Bayesian probability to determine whether an incoming mail is spam ...
*Bayesian probability
Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification ...
* Bayesian procedures
*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 consider ...
* Bayesian program synthesis
*Bayesian quadrature
Bayesian quadrature
is a method for approximating intractable integration problems. It falls within the class of probabilistic numerical methods. Bayesian quadrature views numerical integration as a Bayesian inference task, where function eval ...
*Bayesian regret
In stochastic game theory, Bayesian regret is the expected difference ("regret") between the utility of a Bayesian strategy and that of the optimal strategy (the one with the highest expected payoff).
The term ''Bayesian'' refers to Thomas Baye ...
*Bayesian search theory
Bayesian search theory is the application of Bayesian statistics to the search for lost objects. It has been used several times to find lost sea vessels, for example USS Scorpion (SSN-589), USS ''Scorpion'', and has played a key role in the recover ...
*Bayesian spam filtering
Naive Bayes classifiers are a popular statistical technique of e-mail filtering. They typically use bag-of-words features to identify email spam, an approach commonly used in text classification.
Naive Bayes classifiers work by correlating t ...
*Bayesian statistics
Bayesian statistics is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a ''degree of belief'' in an event. The degree of belief may be based on prior knowledge about the event, ...
*Bayesian structural time series
Bayesian structural time series (BSTS) model is a statistical technique used for feature selection, time series forecasting, nowcasting, inferring causal impact and other applications. The model is designed to work with time series data.
The mode ...
* Bayesian support-vector machine
* Bayesian survival analysis
* Bayesian template estimation
*Bayesian tool for methylation analysis Bayesian tool for methylation analysis, also known as BATMAN, is a statistical tool for analysing methylated DNA immunoprecipitation (MeDIP) profiles. It can be applied to large datasets generated using either oligonucleotide arrays (MeDIP-chip) or ...
*Bayesian vector autoregression In statistics and econometrics, Bayesian vector autoregression (BVAR) uses Bayesian methods to estimate a vector autoregression (VAR) model. BVAR differs with standard VAR models in that the model parameters are treated as random variables, with ...
*Dynamic Bayesian network
A Dynamic Bayesian Network (DBN) is a Bayesian network (BN) which relates variables to each other over adjacent time steps. This is often called a ''Two-Timeslice'' BN (2TBN) because it says that at any point in time T, the value of a variable c ...
*International Society for Bayesian Analysis
The International Society for Bayesian Analysis (ISBA) is a society with the goal of promoting Bayesian analysis for solving problems in the sciences and government. It was formally incorporated as a not for profit corporation by economist Arnold ...
* Perfect Bayesian equilibrium (PBE)
*Quantum Bayesianism
In physics and the philosophy of physics, quantum Bayesianism is a collection of related approaches to the interpretation of quantum mechanics, of which the most prominent is QBism (pronounced "cubism"). QBism is an interpretation that takes an a ...
*Recursive Bayesian estimation
In probability theory, statistics, and machine learning, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach for estimating an unknown probability density function (PDF) recursively over time using inco ...
* Robust Bayesian analysis
* Variable-order Bayesian network
*Variational Bayesian methods
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They are typically used in complex statistical models consisting of observed variables (usually ...
See also
*Banburismus
Banburismus was a cryptanalytic process developed by Alan Turing at Bletchley Park in Britain during the Second World War. It was used by Bletchley Park's Hut 8 to help break German ''Kriegsmarine'' (naval) messages enciphered on Enigma machin ...
, a cryptanalytic process
*Bayesian approaches to brain function Bayesian approaches to brain function investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close to the optimal prescribed by Bayesian statistics. This term is used in behavioural sciences and n ...
* Bayesian inference in marketing
* Bayesian inference in motor learning
*Bayesian inference using Gibbs sampling
Bayesian inference using Gibbs sampling (BUGS) is a statistical software for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods. It was developed by David Spiegelhalter at the Medical Research Council Biostatistics Unit i ...
(BUGS)
*Bayesian interpretation of kernel regularization
Within bayesian statistics for machine learning, kernel methods arise from the assumption of an inner product space or similarity structure on inputs. For some such methods, such as support vector machines (SVMs), the original formulation and its r ...
*Bayesian tool for methylation analysis Bayesian tool for methylation analysis, also known as BATMAN, is a statistical tool for analysing methylated DNA immunoprecipitation (MeDIP) profiles. It can be applied to large datasets generated using either oligonucleotide arrays (MeDIP-chip) or ...
(BATMAN)
*Conditional Probability
In probability theory, conditional probability is a measure of the probability of an event occurring, given that another event (by assumption, presumption, assertion or evidence) has already occurred. This particular method relies on event B occu ...
*Credibility theory
Credibility theory is a form of statistical inference used to forecast an uncertain future event developed by Thomas Bayes. It is employed to combine multiple estimates into a summary estimate that takes into account information on the accuracy o ...
* Evidence under Bayes' theorem
*Dempster–Shafer theory
The theory of belief functions, also referred to as evidence theory or Dempster–Shafer theory (DST), is a general framework for reasoning with uncertainty, with understood connections to other frameworks such as probability, possibility and i ...
, a generalization of Bayes' theorem.
* History of Bayesian statistics
*Inverse probability
In probability theory, inverse probability is an obsolete term for the probability distribution of an unobserved variable.
Today, the problem of determining an unobserved variable (by whatever method) is called inferential statistics, the method o ...
* Inverse resolution
*Polytree
In mathematics, and more specifically in graph theory, a polytree (also called directed tree, oriented tree; . or singly connected network.) is a directed acyclic graph whose underlying undirected graph is a tree. In other words, if we replace its ...
References
External links
* {{cite journal
, last = Fienberg , first = Stephen
, date = 2006
, title = When did Bayesian inference become "Bayesian"?
, journal = Bayesian Analysis
, pages = 1–41
, citeseerx = 10.1.1.124.8632
Bayes, Thomas