Converting Graphic Relationships into Conditional Probabilities in Bayesian Network

Abstract

Bayesian network (BN) is a powerful mathematical tool for prediction and diagnosis applications. A large Bayesian network can constitute many simple networks, which in turn are constructed from simple graphs. A simple graph consists of one child node and many parent nodes. The strength of each relationship between a child node and a parent node is quantified by a weight and all relationships share the same semantics such as prerequisite, diagnostic, and aggregation. The research focuses on converting graphic relationships into conditional probabilities in order to construct a simple Bayesian network from a graph. Diagnostic relationship is the main research object, in which sufficient diagnostic proposition is proposed for validating diagnostic relationship. Relationship conversion is adhered to logic gates such as AND, OR, and XOR, which are essential features of the research.

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