Patent Document ID: 7650272
Application ID: 10692697

Base Claim:
1. A computer implemented method for automatically evaluating Bayesian network models for decision support comprising an act of causing a computer to perform operations of: receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes, where the conclusion nodes are linked with the evidence nodes by causal dependency links, and where the evidence nodes have evidence states and the conclusion nodes have conclusion states; setting the states of the conclusion nodes to desired conclusion states and determining, by propagating down the causal dependency links, a corresponding probability of occurrence of evidence states of the evidence nodes and producing, from the probability of occurrence, a plurality of samples of most likely states of the evidence nodes; setting the states of the evidence nodes to states corresponding to the plurality of samples of the evidence states, and propagating the evidence states back up the causal dependency links to the conclusion nodes, to obtain a plurality of probabilities of the resulting states of the conclusion nodes; and outputting a representation of the plurality of the probabilities of the resulting states of the conclusion nodes.

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Claim 13:
13. A method for automatically evaluating Bayesian network models for decision support as set forth in claim 1 , wherein the outputted representation is a matrix of averages of probabilities of the conclusion states for implicated conclusions versus a selected set of combinations of conclusion states; whereby a user can determine an accuracy of the BN model's propensity to yield proper conclusions.