Gourt > Computers > Artificial Intelligence > Belief Networks (4)
A Bayesian network or Bayesian belief network or just belief network is a form of probabilistic graphical model. Bayesian network represents joint probability distribution of a set of variables with explicit independency assumptions.
Definition
A Bayesian network is a directed acyclic graph of nodes representing variables and arcs representing dependence relations among the variables. If there is an arc from node A to another node B, then values of variable B depend directly on values of A and A is called a parent of B. If a node has a known value, it is said to be an evidence node. A node can represent any kind of variable, be it an observed measurement, a parameter, a latent variable, or a hypothesis. Nodes are not restricted to representing random variables; this is what is "Bayesian" about a Bayesian network. Let the variables be X(1), ..., X(n). Let parents(A) be the parents of the node A. Then the joint distribution for X(1) through X(n) is represented as the product of the probability distributions for i = 1 to n. If X has no parents, its probability distribution is said to be unconditional, otherwise it is conditional.
More on [ Bayesian network ]
Neural Networks :: Artificial Intelligence
Probability :: Math
Bayesian Analysis :: Statistics
Reading up on Bayesian networks
k0emt (Bryan Nehl) Sat, 07 Nov 2009 05:19:04 -0000
Reading up on Bayesian networks
Quantum Computing Primer for Spies « Quantum Bayesian Networks http://bit.ly/4uQrng
chokha (chokha) Fri, 06 Nov 2009 21:55:12 -0000
Quantum Computing Primer for Spies « Quantum Bayesian Networks http://bit.ly/4uQrng
Experimental Physics and Quantum Computing « Quantum Bayesian Networks http://bit.ly/2pWzad
Kjmastaw (Kevin Mastaw) Thu, 05 Nov 2009 06:40:38 -0000
Experimental Physics and Quantum Computing « Quantum Bayesian Networks http://bit.ly/2pWzad
Experimental Physics and Quantum Computing « Quantum Bayesian Networks http://bit.ly/4sStLr
chokha (chokha) Wed, 04 Nov 2009 20:29:20 -0000
Experimental Physics and Quantum Computing « Quantum Bayesian Networks http://bit.ly/4sStLr
Just finished a problem I have been working on for 3 hours. "Most Probable Explanation" ... Bayesian Networks, as always.
DataJunkie (Ryan Rosario) Mon, 02 Nov 2009 05:53:44 -0000
Just finished a problem I have been working on for 3 hours. "Most Probable Explanation" ... Bayesian Networks, as always.
New book: Modeling and Reasoning with Bayesian Networks by Adnan Darwiche from Cambridge University Press. http://bit.ly/3Xdkmv
chengweiwei (Weiwei Cheng) Thu, 29 Oct 2009 10:58:59 -0000
New book: Modeling and Reasoning with Bayesian Networks by Adnan Darwiche from Cambridge University Press. http://bit.ly/3Xdkmv
A Brief Introduction to Graphical Models and Bayesian Networks - Kevin Murphy's tutorial, including a recommended reading list.
An Introduction to Bayesian Networks and Their Contemporary Applications - A survey and tutorial by Daryle Niedermayer - covers material on Bayesian inference in general and selected industrial applications of graphical models
Association for Uncertainty in Artificial Intelligence - Main association for belief network researchers. Runs the annual Uncertainty in Artificial Intelligence (UAI) conferences, and the UAI mailing list.
Meta Description: [ Web site for the Association for Uncertainty in Artificial Intelligence ]
Bayesian Network Repository - Maintained by Gal Elidan - over a dozen publicly available networks with documentation, in several popular interchange formats
Belief Networks and Variational Methods : Amos Storkey - Dynamic Trees are mixtures of tree structured belief networks, and are used as models for image segmentation and tracking.
Meta Description: [ Tutorial: Introduction to Belief Networks. A simple illustrated tutorial on belief networks (or Bayesian networks), with links and references for further reading. This tutorial focusses on the introductory issues of design of Bayes nets, inference in belief nets, and learning belief network param... ]
Cause, chance and Bayesian statistics - Briefing document with a short survey of Bayesian statistics
Meta Description: [ briefing document to facilitate understanding Bayesian statistics. The statistical theory developed by Thomas Bayes enables analysis of conditional and marginal probabilities. Bayesian statistics enables logical inference ]
Daphne's Approximate Group of Students (DAGS) - Daphne Koller's research group on probabilistic representation, reasoning, and learning at Stanford University
Meta Description: [ DAGS - Daphne Koller's Research Group working on Probabilistic Reasoning with Bayesian Networks, Markov Decision Processes and Probabilistic Relational Models. ]
Decision Systems Lab (DSL) - Research group at the University of Pittsburgh with links to books and software on probabilistic, decision-theoretic, and econometric graphical models
LAPLACE Group - Bayesian Models for Perception, Inference and Action - Probabilistic reasoning and genetic algorithms for perception, inference and action: Bayesian cognitive and brain models, software for robotics, probabilistic inference engine
Learning Bayesian Networks from Data - Slides and additional notes from a tutorial by Nir Friedman and Daphne Koller on automated learning of belief networks, given at the Neural Information Processing Systems (NIPS-2001) conference
Qualitative Verbal Explanations in Bayesian Belief Networks - Paper about combining probabilistic models and human-intuitive approaches to modeling uncertainty by generating qualitative verbal explanations of reasoning.
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Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference - Article published in JAIR (Journal of AI Research) about a way to implement belief networks by compiling networks into arithmetic expressions and then answering queries using an evaluation algorithm.
Meta Description: [ Query DAGs: A Practical Paradigm for Implementing Belief-Network Inference ]
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Conversation Scene Analysis: Conversation Scene Analysis with Dynamic Bayesian Network based on Visual Head Tracking IEEE ICME'06, July, 2006 |
