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Evaluating Probabilistic Inference Techniques: A Question of “When,” not “Which”

  • Cory J. Butz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6929)

Abstract

Historically, it has been claimed that one inference algorithm or technique, say A, is better than another, say B, based on the running times on a test set of Bayesian networks. Recent studies have instead focusing on identifying situations where A is better than B, and vice versa. We review two cases where competing inference algorithms (techniques) have been successfully applied together in unison to exploit the best of both worlds. Next, we look at recent advances in identifying structure and semantics. Finally, we present possible directions of future work in exploiting structure and semantics for faster probabilistic inference.

Keywords

Bayesian Network Directed Acyclic Graph Inference Algorithm Cost Measure Probabilistic Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Cory J. Butz
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaCanada

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