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Bayesian statistics
Posterior = Likelihood × Prior ÷ Evidence
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Bayesian inference
Bayesian probability
Bayes' theorem
Bernstein–von Mises theorem
Coherence
Cox's theorem
Cromwell's rule
Principle of indifference
Principle of maximum entropy
Model building
Weak prior ... Strong prior
Conjugate prior
Linear regression
Empirical Bayes
Hierarchical model
Posterior approximation
Markov chain Monte Carlo
Laplace's approximation
Integrated nested Laplace approximations
Variational inference
Approximate Bayesian computation
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Credible interval
Maximum a posteriori estimation
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Evidence lower bound
Nested sampling
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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).[1] While it is one of several forms of causal notation, causal networks are special cases of Bayesian networks. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases.
Efficient algorithms can perform inference and learning in Bayesian networks. Bayesian networks that model sequences of variables (e.g. speech signals or protein sequences) are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams.
^Ruggeri, Fabrizio; Kenett, Ron S.; Faltin, Frederick W., eds. (2007-12-14). Encyclopedia of Statistics in Quality and Reliability (1 ed.). Wiley. p. 1. doi:10.1002/9780470061572.eqr089. ISBN 978-0-470-01861-3.
A Bayesiannetwork (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents...
dynamic Bayesiannetwork (DBN) is a Bayesiannetwork (BN) which relates variables to each other over adjacent time steps. A dynamic Bayesiannetwork (DBN)...
learning. Bayesiannetworks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesiannetworks. Generalizations...
the classifier its name. These classifiers are among the simplest Bayesiannetwork models. Naive Bayes classifiers are highly scalable, requiring a number...
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...
dynamic decision networks, game theory and mechanism design. Bayesiannetworks are a tool that can be used for reasoning (using the Bayesian inference algorithm)...
A Markov network or MRF is similar to a Bayesiannetwork in its representation of dependencies; the differences being that Bayesiannetworks are directed...
decision network) is a compact graphical and mathematical representation of a decision situation. It is a generalization of a Bayesiannetwork, in which...
a fallback Dynamic Bayesiannetwork – Probabilistic graphical model International Society for Bayesian Analysis Perfect Bayesian equilibrium – Solution...
Markov blanket. The Markov boundary of a node A{\displaystyle A} in a Bayesiannetwork is the set of nodes composed of A{\displaystyle A}'s parents, A{\displaystyle...
Bayesian approaches to brain function investigate the capacity of the nervous system to operate in situations of uncertainty in a fashion that is close...
neural networks are approaches used in machine learning to build computational models which learn from training examples. Bayesian neural networks merge...
graphical representations of distributions are commonly used, namely, Bayesiannetworks and Markov random fields. Both families encompass the properties of...
used to determine the causes of symptoms, mitigations, and solutions. Bayesiannetwork Complex event processing Diagnosis (artificial intelligence) Event...
In probability theory, statistics, and machine learning, recursive Bayesian estimation, also known as a Bayes filter, is a general probabilistic approach...
regression Bayesian model comparison – see Bayes factor Bayesian multivariate linear regression BayesiannetworkBayesian probability Bayesian search theory...
participants.: 356 Any causal model can be implemented as a Bayesiannetwork. Bayesiannetworks can be used to provide the inverse probability of an event...
decision trees and Bayesiannetworks. One can also construct co-expression networks between module eigengenes (eigengene networks), i.e. networks whose nodes...
For example, it can be used for modeling and analysing trust networks and Bayesiannetworks. Arguments in subjective logic are subjective opinions about...
Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution...
Markov assumption, is an assumption made in Bayesian probability theory, that every node in a Bayesiannetwork is conditionally independent of its nondescendants...
natural language processing, latent Dirichlet allocation (LDA) is a Bayesiannetwork (and, therefore, a generative statistical model) for modeling automatically...
instance, Bayesiannetworks, dynamic Bayesiannetworks, Kalman filters or hidden Markov models. Indeed, Bayesian Programming is more general than Bayesian networks...
Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning. They...
Bayesian probability (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is an interpretation of the concept of probability, in which, instead of frequency or...
In physics and the philosophy of physics, quantum Bayesianism is a collection of related approaches to the interpretation of quantum mechanics, the most...
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