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Main.BayesianBeliefNetworkr1.3 - 11 Jul 2006 - 14:43 - JohnTester15topic end

---+ Bayesian Belief Network
A 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 Algorithms
The 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
   * [[StochasticSimulation]]
   * [[VariationalMethods]]
   * [[Search-basedAlgorithms]]
---+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

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