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Canadian Conference on Artificial Intelligence

Canadian AI 2016: Advances in Artificial Intelligence pp 285–297Cite as

Compression of General Bayesian Net CPTs

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9673))

Abstract

Non-Impeding Noisy-AND (NIN-AND) Tree (NAT) models offer a highly expressive approximate representation for significantly reducing the space of Bayesian Nets (BNs). They can also significantly improve efficiency of BN inference, as shown for binary NAT models. To enable these advantages for general BNs, advancements on three technical challenges are made in this work. We overcome the limitation of well-defined Pairwise Causal Interaction (PCI) bits and present a flexible PCI pattern extraction from general target Conditional Probability Tables (CPTs). We extend parameter estimation for binary NAT models to constrained gradient descent for compressing target CPTs into multi-valued NAT models. The effectiveness of the compression is demonstrated experimentally. A novel framework is also developed for PCI pattern extraction when persistent leaky causes exist.

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Acknowledgement

Financial support from NSERC Discovery Grant is acknowledged. We thank anonymous reviewers. We apologize for not moving explanations of figures and tables from text to captions, as it does not appear feasible to us.

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Authors and Affiliations

  1. University of Guelph, Guelph, Canada

    Yang Xiang & Qian Jiang

Authors
  1. Yang Xiang
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  2. Qian Jiang
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Corresponding author

Correspondence to Yang Xiang .

Editor information

Editors and Affiliations

  1. Lakehead University, Thunder Bay, Ontario, Canada

    Richard Khoury

  2. National Research Council Canada , Ottawa, Canada

    Christopher Drummond

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© 2016 Springer International Publishing Switzerland

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Xiang, Y., Jiang, Q. (2016). Compression of General Bayesian Net CPTs. In: Khoury, R., Drummond, C. (eds) Advances in Artificial Intelligence. Canadian AI 2016. Lecture Notes in Computer Science(), vol 9673. Springer, Cham. https://doi.org/10.1007/978-3-319-34111-8_35

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  • DOI: https://doi.org/10.1007/978-3-319-34111-8_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34110-1

  • Online ISBN: 978-3-319-34111-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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