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Virtual Representation of IID Observations in Bayesian Belief Networks

Mislevy, Robert J.
Publication Year:
Report Number:
ETS Research Memorandum
Document Type:
Page Count:
Subject/Key Words:
Bayesian Statistics, Statistical Analysis


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.

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