This invention
patent application describes mathematical methods to evaluate and validate the numbers in the
conditional probability tables of a
Bayesian network. Using the methods described here, the nodes of interest in the network could be evaluated for validity of the information they contain and errors could be detected by domain experts or knowledge engineers very easily. If there is a disagreement between knowledge engineers or domain experts belief of what the interaction should be and what is detected in the behavior of nodes selected for validation as shown in the reports, then, those errors could be easily located in the structure of the
Bayesian network by pin pointing the table, column and row of the problematic
cell. Then, the knowledge engineer or domain expert could modify the numbers to reflect the correct behavior. These methods also provide significant insight into the structure and efficiency of the structural design of the
Bayesian network as well as
value of information in the network. Using this information,
hypothesis oriented application of Bayesian network is possible and evidence most relevant to the
hypothesis of interest could be instantiated first. Additionally, the shortest path to rule-out or rule-in of a
hypothesis could be known before the network is used. Applications of these methods in
computer software could allow for streamlined and semi-automated design and validation process and construction of Bayesian networks. Furthermore, by using an almost reverse process, information about
a domain can be captured and sorted lists prepared which in turn will be used to prepare a preliminary Bayesian network. Data elicitation using the network created in this fashion will complete the structure and probability tables of the Bayesian network.