Conditional probability tables for Bayesian belief networks

摘要

An apparatus for making probabilistic inferences based on a belief network includes a processing system configured to receive as input one or more parameters of a causal influence model. The belief network has a child node Y and one or more parent nodes Xi (i=1, . . . , n) for the child node Y. The causal influence model describes the influence of the parent nodes Xi on possible states of the child node Y. The processing system is further configured to use a creation function to convert the parameters of the causal influence model into one or more entries of a conditional probability table. The conditional probability table provides a probability distribution for all the possible states of the child node Y, for each combination of possible states of the parent nodes Xi.