A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents...
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dynamic Bayesian network (DBN) is a Bayesian network (BN) which relates variables to each other over adjacent time steps. A dynamic Bayesian network (DBN)...
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Machine learning (section Bayesian networks)
learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations...
135 KB (14,775 words) - 00:31, 1 June 2024
Naive Bayes classifier (redirect from Naive Bayesian classifier)
the classifier its name. These classifiers are among the simplest Bayesian network models. Naive Bayes classifiers are highly scalable, requiring a number...
35 KB (5,489 words) - 21:59, 3 June 2024
Influence diagram (redirect from Decision network)
decision network) is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesian network, in which...
12 KB (1,514 words) - 02:58, 13 December 2023
dynamic decision networks, game theory and mechanism design. Bayesian networks are a tool that can be used for reasoning (using the Bayesian inference algorithm)...
220 KB (22,414 words) - 17:55, 3 June 2024
Bayesian statistics (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is a theory in the field of statistics based on the Bayesian interpretation of probability...
19 KB (2,393 words) - 14:28, 26 February 2024
List of things named after Thomas Bayes (redirect from Bayesian)
Bayesian analysis – Type of sensitivity analysis Variable-order Bayesian network Variational Bayesian methods – Mathematical methods used in Bayesian...
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Markov random field (redirect from Markov network)
A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed...
19 KB (2,777 words) - 08:08, 29 April 2024
neural networks are approaches used in machine learning to build computational models which learn from training examples. Bayesian neural networks merge...
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Markov blanket (category Bayesian networks)
Markov blanket. The Markov boundary of a node A {\displaystyle A} in a Bayesian network is the set of nodes composed of A {\displaystyle A} 's parents, A {\displaystyle...
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In probability theory, statistics, and machine learning, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach...
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and hypertree networks Bayesian network Bridges of Königsberg Computer network Ecological network Electrical network Gene regulatory network Global shipping...
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Graphical model (category Bayesian statistics)
graphical representations of distributions are commonly used, namely, Bayesian networks and Markov random fields. Both families encompass the properties of...
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Bayesian probability (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is an interpretation of the concept of probability, in which, instead of frequency or...
33 KB (3,413 words) - 03:17, 25 March 2024
Diagnosis (section Computer science and networking)
used to determine the causes of symptoms, mitigations, and solutions. Bayesian network Complex event processing Diagnosis (artificial intelligence) Event...
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regression Bayesian model comparison – see Bayes factor Bayesian multivariate linear regression Bayesian network Bayesian probability Bayesian search theory...
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Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution...
21 KB (3,630 words) - 23:39, 17 August 2023
variable and each edge captures dependencies among variables. Unlike Bayesian networks, DNs may contain cycles. Each node is associated to a conditional...
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Causal model (section Bayesian network)
participants.: 356 Any causal model can be implemented as a Bayesian network. Bayesian networks can be used to provide the inverse probability of an event...
48 KB (6,139 words) - 13:55, 2 June 2024
decision trees and Bayesian networks. One can also construct co-expression networks between module eigengenes (eigengene networks), i.e. networks whose nodes...
28 KB (3,109 words) - 21:57, 2 December 2023
network escape from local minima. Stochastic neural networks trained using a Bayesian approach are known as Bayesian neural networks. In a Bayesian framework...
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Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They...
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Variable-order Bayesian network (VOBN) models provide an important extension of both the Bayesian network models and the variable-order Markov models....
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Bayesian approaches to brain function investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close...
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Solomonoff and the MML work of Chris Wallace, and see Dowe's "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness"...
93 KB (10,770 words) - 04:23, 15 May 2024
Bayesian inference (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is a method of statistical inference in which Bayes' theorem is used to update the probability...
66 KB (8,785 words) - 23:55, 28 March 2024
Outline of machine learning (section Bayesian)
neighbor Boosting SPRINT Bayesian networks Naive Bayes Hidden Markov models Hierarchical hidden Markov model Bayesian statistics Bayesian knowledge base Naive...
41 KB (3,582 words) - 23:42, 27 May 2024
Causal Markov condition (category Bayesian networks)
Markov assumption, is an assumption made in Bayesian probability theory, that every node in a Bayesian network is conditionally independent of its nondescendants...
5 KB (640 words) - 07:03, 17 September 2023
Subjective logic (redirect from Subjective Bayesian networks)
For example, it can be used for modeling and analysing trust networks and Bayesian networks. Arguments in subjective logic are subjective opinions about...
18 KB (2,463 words) - 13:08, 4 April 2024