Abstract
AbstractWe show that a conditional probability table (CPT) is obtained after every multiplication and every marginalization step when eliminating variables from a discrete Bayesian network. The main advantage of our work is an improvement in presentation. The probability distributions constructed during variable elimination in Bayesian networks have always been denoted as potentials. Since CPTs are a special case of potential, our description is more precise and readable.KeywordsNormal FormBayesian NetworkDirected Acyclic GraphJoint Probability DistributionExpanded FormThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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