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A pseudo independent (PI) model is a probabilistic domain model (PDM) whereproper subsets of a set of collectively dependent variables display marginalindependence. PI models cannot be learned correctly by many algorithms thatrely on a single link search. Earlier work on learning PI models has suggesteda straightforward multi-link search algorithm. However, when a domain containsrecursively embedded PI submodels, it may escape the detection of such analgorithm. In this paper, we propose an improved algorithm that ensures thelearning of all embedded PI submodels whose sizes are upper bounded by apredetermined parameter. We show that this improved learning capability onlyincreases the complexity slightly beyond that of the previous algorithm. Theperformance of the new algorithm is demonstrated through experiment.

Learning Belief Networks in Domains with Recursively Embedded Pseudo Independent Submodels