Virtual Representation of IID Observations in Bayesian Belief Networks.

摘要

Local computation for updating Bayesian belief networks proceeds in the context of a join tree, consisting of subsets of interrelated variables (cliques) joined by their intersection sets in a singly-connected graphical structure. When multiple independent and identically-distributed (IID) observations of a variable can be made, identically structured cliques corresponding to each potential observation appear as terminal nodes in the join tree. This note shows how it is possible to absorb information from an indefinite number of observations of this type without preconstructing and manipulating cliques for all potential observations. An update replace strategy carries the necessary information with only two nodes for a family of IID observations of a variable at any point in time. Bayesian inference networks, Causal probability networks, Expert systems, Influence diagrams, Intelligent tutoring systems, Local computation.