Abstract: Fault trees are automatically converted to Bayesian networks for assisting in system reliability, failure analysis and diagnostics by using information from the fault tree structure to create the Bayesian network structure, creating parameters of the Bayesian network using information from the fault tree, obtaining information about observation nodes for the Bayesian network from a list of observations that augments information contained in the fault tree, and inserting the observation nodes into the Bayesian network. The fault tree is pre-processed into an intermediate format prior to conversion that may include adding reliability values from a separate text document when the fault tree is in such format that requires it.

Abstract: A method, apparatus, and computer program product are presented for automatically evaluating Bayesian network models. Operations performed comprise receiving a Bayesian Network (BN) model including evidence nodes and conclusion nodes that 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. The states of conclusion nodes are set to desired conclusion states and corresponding probabilities of occurrence of evidence states are determined by propagating these states down the causal dependency links. Thus, samples of most likely states of the evidence nodes are generated. Then, states of the evidence nodes are set corresponding to the samples of the evidence states. These states are propagated back up the causal dependency links to obtain probabilities of the resulting states of the conclusion nodes. Finally, a representation is outputted for the probabilities of the states of the conclusion nodes.

Abstract: The present invention converts decision flowcharts into decision probabilistic graphs on a data processing system. First, a decision flowchart is received, having evidence nodes, a root evidence node, and outcome nodes. The outcome nodes are related to the evidence nodes by conclusion links. Next, an operation is performed, generating a probabilistic graph based on the flowchart. The graph includes an aggregate outcome node having outcome states, with each outcome state representing an outcome node of the flowchart; a plurality of test nodes, each matching an evidence node in the flowchart, and each test state matching a conclusion link from the evidence node in the flowchart, and causal links between the aggregate outcome node and the evidence nodes. Prior probabilities are calculated for outcome states based on predetermined likelihoods.