Adrian Y. W. Cheuk and Craig Boutilier
Department of Computer Science
University of British Columbia
Vancouver, BC, CANADA, V6T 1Z4
email: cheuk,cebly@cs.ubc.ca
Abstract
We present an algorithm for arc reversal in Bayesian networks with
tree-structured conditional probability tables, and consider
some of its advantages, especially for the simulation of
dynamic probabilistic networks. In particular, the method allows one
to produce CPTs for nodes involved in the reversal that
exploit regularities in the conditional distributions. We argue that
this approach alleviates some of the overhead associated with
arc reversal, plays an important role in evidence integration
and can be used to restrict sampling of variables in DPNs.
We also provide an algorithm that detects the
dynamic irrelevance of state variables in forward
simulation. This algorithm exploits the structured CPTs in a reversed
network to determine, in a time-independent fashion, the conditions
under which a variable does or does not need to be sampled.
To appear, UAI-97, Providence, RI
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