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Stochastic simulation in dynamic probabilistic networks using compact representation

University of British Columbia

In recent years, researchers in the A l domain have used Bayesian belief networks to build models of expert opinion. Though computationally expensive deterministic algorithms have been devised, it has been shown that exact probabilistic inference in belief networks, especially multiply connected ones, is intractable. In view of this, various approximation methods based on stochastic simulation appeared in an attempt to perform efficient approximate inference in large and richly interconnected models. However, due to convergence problems, approximation in dynamic probabilistic networks has seemed unpromising. Reversing arcs into evidence nodes can improve convergence performance in simulation, but the resulting exponential increase in network complexity and, in particular, the size of the conditional probability tables (CPTs) can often render this evidence reversal method computationally inefficient. In this thesis, we describe a structured simulation algorithm that uses the evidence reversal technique based on a tree-structured representation for CPTs. Most real systems exhibit a large amount of local structure. The tree can reduce network complexity by exploiting this structure to keep CPTs in a compact way even after arcs have been reversed. The tree also has a major impact on improving computational efficiency by capturing context-specific independence during simulation. Experimental results show that in general our structured evidence reversal algorithm improves convergence performance significantly while being both spatially and computationally much more efficient than its unstructured counterpart.

Thesis/Dissertation

application/pdf

2009-03-13

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http://hdl.handle.net/2429/6018

Computer Science

University of British Columbia

UBCV

DSpace