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Inference in Bayesian Networks
- Bayesian networks can model conditional independencies between variables based on the network structure. Each variable is conditionally independent of its non-descendants given its parents. - The d-separation algorithm allows determining if two variables are conditionally independent given some evidence by checking if all paths between them are "blocked". - For trees/forests where each node has at most one parent, inference can be done efficiently in linear time by decomposing probabilities and passing messages between nodes.
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