We construct probabilistic networks to support and solve problems of reasoning and decision making under uncertainty. In problems of reasoning under uncertainty the posterior probability of a single hypothesis variable is sometimes of interest. When the evidence set consists of a large number of findings or even when it consists of only a small number of findings questions concerning the impact of subsets of the evidence on the hypothesis or a competing hypothesis emerge.
KeywordsPosterior Probability Bayesian Network Sensitivity Function Conditional Probability Distribution Posterior Probability Distribution
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