## Bayesian Belief NetworkA Bayesian belief network (also known as a BayesianNetwork, a BeliefNetwork or a BayesNet) is a representation of dependence (and so also independence) of random variables. It is named after ThomasBayes. |

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## Bayesian Belief NetworkA Bayesian belief network (also known as a BayesianNetwork, a BeliefNetwork or a BayesNet) is a representation of dependence (and so also independence) of random variables. It is named after ThomasBayes.A belief network can be described in terms of - structure: a directed acyclic graph where the nodes are random variables and a domain for each random variable
- function: how each variable depends on its parents, such as using a table, a decision tree, a "noisy or", or some parametrized form
- number: the actual numbers needed to specify a conditional probability of each variable given its parents
## History
## Algorithms for Inference in Belief networks## Exact AlgorithmsThe main exact algorithms are- VariableElimination
^{?}which exploits the sparseness of the graph to answer single queries - CliqueTreePropagation
^{?}which creates a secondary structure to give the posterior on each variable and allow for the incorporation of new evidence. - RecursiveConditioning
^{?}that allows for the tradeoff between time and space
## Approximate Algorithms## Learning Belief networks
## Causality
## Alternative Related Representations- A DecisionNetwork
^{?}adds decision varibles and utility variables for making sequential decisions - A CausalNetwork makes predictions of interventions
## Industrial Applications |

Revision r1.1 - 21 Nov 2005 - 22:53 - MarkCrowley

Revision r1.2 - 23 Mar 2006 - 00:52 - MarkCrowley

Revision r1.2 - 23 Mar 2006 - 00:52 - MarkCrowley