Bayesian network

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Inference and learning

Sufficient statistic Bayes' theorem Variable elimination Clique tree propagation Space–time tradeoff

30 related topics

Definitions and concepts

Random variables Probability density function Product measure Joint distribution Chain rule

14 related topics

Statistical introduction

Bayesian analysis Prior probability Likelihood Hierarchical Bayes model Standard deviation

10 related topics

Inference complexity and approximation algorithms

NP-hard Michael Luby Deterministic algorithm Absolute error Randomized algorithm

8 related topics

Topics to explore

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Overview

Probabilistic graphical model
Conditional dependencies Conditional dependence
Directed acyclic graph
Causal notation
Inference
Learning Machine learning
Speech signals Speech recognition
Protein sequences Peptide sequence
Dynamic Bayesian networks Dynamic Bayesian network
Influence diagrams Influence diagram
Markov blanket

Graphical model

Bayesian Bayesian probability
Latent variables Latent variable
Conditionally independent Conditional independence
Probability function Probability distribution
Parent Glossary of graph theory
Boolean variables Boolean data type
Markov networks Markov network

Example

Joint probability function Joint probability distribution
Chain rule of probability
Conditional probability
Nuisance variables Nuisance variable
Conditional probability tables (CPTs) Conditional probability table
D-separates Bayesian network
Simpson's paradox

Inference and learning

Sufficient statistic
Bayes' theorem
Variable elimination
Clique tree propagation Junction tree algorithm
Space–time tradeoff
Treewidth
Approximate inference
Importance sampling
Markov chain Monte Carlo
Loopy belief propagation
Generalized belief propagation
Variational methods Variational Bayes
Gaussian distributions Normal distribution
Principle of maximum entropy
Entropy Information entropy
Entropy rate
Maximum likelihood
Posterior probability
Expectation-maximization algorithm
Pearl Judea Pearl
Scoring function
BIC Bayesian information criterion
Exhaustive search
Superexponential Tetration
Local minima Maxima and minima
Mutual information
Integer programming
Cutting planes Cutting-plane method
MLE Maximum likelihood estimate
K-tree

Statistical introduction

Bayesian analysis Bayesian statistics
Prior probability
Likelihood Likelihood function
Hierarchical Bayes model Bayesian hierarchical modeling
Standard deviation
Improper priors Improper prior
Shrink Shrinkage estimator
Jeffreys prior
Expected loss Loss function
Inadmissible Admissible decision rule

Definitions and concepts

Random variables Random variable
Probability density function
Product measure
Joint distribution
Chain rule Chain rule (probability)
Topological ordering
Acyclic Cycle (graph theory)
NP-hard NP-hardness
Combinatorial explosion
Simple undirected graph Graph (discrete mathematics)
Intersection Intersection number (graph theory)
Independence numbers Independent set (graph theory)
Causal Causal graph
Causal Causality

Inference complexity and approximation algorithms

Software

History

Neapolitan Richard E. Neapolitan

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Map overview Semantic statistics

Nodes102

Edges101

Triples167

Avg. degree1.98

Density0.019608

Components1

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Bayesian network

Top relations

related to Further reading · 31

Bayesian network → AI Magazine, BayesiaLab, Bayesian, Bayesian Networks, Bayesian USA, Borgelt, Charniak, Chichester, Computational Intelligence, Conrady, Data Mining, Franklin, Graphical Models, Held, ISBN, Jouffe, Klawonn, Kruse, Learning, London

related to Inference complexity and approximation algorithms · 14

Bayesian network → At, Bayesian, CNF, Cooper, First, In, Michael Luby, NP-hard, P-complete, Paul Dagum, Roth, Second, Stanford University, This

related to Software · 14

Bayesian network → Bayesian, Commercial, Hamiltonian Monte Carlo, MCMC, No, No-U-Turn, Notable, NUTS, One, Open-source, OpenBUGS, SPSS Modeler, Stan, WinBUGS

related to External links · 13

Bayesian network → An Introduction, AnalogyA, App, Archived, Bayes Model, Bayesian, Bayesian Networks, Contemporary ApplicationsOn-line Tutorial, Explanation, Hierarchical Naive Bayes Model, Monte Carlo, Time Bayesian NetworksBayesian Networks, Wayback Machine

related to Parameter learning · 11

Bayesian network → Analogously, Bayesian, Direct, Gaussian, In, It, Often, Sometimes, The, Under, X's

related to Graphical model · 9

Bayesian network → Any, Bayesian, Boolean, DAGs, Each, For, Formally, Markov, Similar

related to Inferring unobserved variables · 9

Bayesian network → All, AND/OR, Bayes, Bayesian, Because, For, MCMC, The, This

related to Marginal independence structure · 8

Bayesian network → Bayesian, DAG, DAGs, In, Markov, Nevertheless, NP-hard, This

related to Structure learning · 8

Bayesian network → Automatically, Bayesian, BN, DAG, In, Pearl, Rebane, The

related to Developing Bayesian networks · 7

Bayesian network → Bayesian, DAG, Developing, In, Markov, Sometimes, The

Important terminology Word statistics

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Important terminology

bayesian variables network networks displaystyle probability conditional inference set distribution given nodes one learning model causal likelihood example data probabilistic

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
Bayesian network	is a	complete model for its variables and their relationships	0.90	text
Markov networks	instance of	graphs	0.80	text
the variable τ	instance of	particularly on scale variables at higher levels of the hierarchy	0.80	text
the Jeffreys prior often do not work	instance of	The usual priors	0.80	text
because the posterior distribution will not be normalizable	instance of	The usual priors	0.80	text
estimates made by minimizing the expected loss will be inadmissible	instance of	The usual priors	0.80	text
equal intersection	instance of	are encoded by a simple undirected graph with special properties	0.80	text
independence numbers.Developing Bayesian networksDeveloping a Bayesian network often begins with creating a DAG G such that X satisfies the local Markov property with respect to G	instance of	are encoded by a simple undirected graph with special properties	0.80	text
independence numbers	instance of	are encoded by a simple undirected graph with special properties	0.80	text
Bayesian network	related to Causal networks	Although Bayesian	0.60	section
Bayesian network	related to Causal networks	Xv	0.60	section
Bayesian network	related to Causal networks	Xu	0.60	section

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Inference and learning

Definitions and concepts

Statistical introduction

Inference complexity and approximation algorithms

Key facts & relationships

Topics to explore

Overview

Graphical model

Example

Inference and learning

Statistical introduction

Definitions and concepts

Inference complexity and approximation algorithms

Software

History

Advanced semantic analysis

Map overview Semantic statistics

Bayesian network

How this topic connects Entity context

Bayesian network

Top relations

Important terminology Word statistics

Important terminology

Entity relationships Subject–Predicate–Object triples

Related concept clusters Concept neighborhoods

Connections between topic areas Semantic bridges