A Standard Approach for Optimizing Belief Network Inference using Query DAGs release_fbqtgmkzvbb7jeazn5a32f3wni

by Adnan Darwiche, Gregory M. Provan

Released as a report .

2013  

Abstract

This paper proposes a novel, algorithm-independent approach to optimizing belief network inference. rather than designing optimizations on an algorithm by algorithm basis, we argue that one should use an unoptimized algorithm to generate a Q-DAG, a compiled graphical representation of the belief network, and then optimize the Q-DAG and its evaluator instead. We present a set of Q-DAG optimizations that supplant optimizations designed for traditional inference algorithms, including zero compression, network pruning and caching. We show that our Q-DAG optimizations require time linear in the Q-DAG size, and significantly simplify the process of designing algorithms for optimizing belief network inference.
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Type  report
Stage   submitted
Date   2013-02-06
Version   v1
Language   en ?
Number  UAI-P-1997-PG-116-123
arXiv  1302.1532v1
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