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Importance Sampling on Bayesian Networks with Deterministic Causalities

  • Conference paper
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6717))

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Abstract

Importance sampling is a powerful approximate inference technique for Bayesian networks. However, the performance tends to be poor when the network exhibits deterministic causalities. Deterministic causalities yield predictable influences between statistical variables. In other words, only a strict subset of the set of all variable states is permissible to sample. Samples inconsistent with the permissible state space do not contribute to the sum estimate and are effectively rejected during importance sampling. Detecting inconsistent samples is NP-hard, since it amounts to calculating the posterior probability of a sample given some evidence. Several methods have been proposed to cache inconsistent samples to improve sampling efficiency. However, cache-based methods do not effectively exploit overlap in the state patterns generated by determinism in a local network structure to compress state information. In this paper, we propose a new algorithm to reduce the overhead of caching by using an adaptive decision tree to efficiently store and detect inconsistent samples. Experimental results show that the proposed approach outperforms existing methods to sample Bayesian networks with deterministic causalities.

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Authors and Affiliations

  1. Department of Computer Science, Florida State University, Tallahassee, FL, 32306, USA

    Haohai Yu & Robert van Engelen

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  1. Haohai Yu
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  2. Robert van Engelen
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Editors and Affiliations

  1. School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, BT9 5BN, Belfast, UK

    Weiru Liu

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Yu, H., van Engelen, R. (2011). Importance Sampling on Bayesian Networks with Deterministic Causalities. In: Liu, W. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2011. Lecture Notes in Computer Science(), vol 6717. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22152-1_13

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  • DOI: https://doi.org/10.1007/978-3-642-22152-1_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22151-4

  • Online ISBN: 978-3-642-22152-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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