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Prolog Issues and Experimental Results of an MCMC Algorithm

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Web Knowledge Management and Decision Support (INAP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2543))

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Abstract

We present a Markov chain Monte Carlo algorithm that operates on generic model structures that are represented by terms found in the computed answers produced by stochastic logic programs. The objective of this paper is threefold (a) to show that SLD-trees are an elegant means for describing prior distributions over model structures (b) to sketch an implementation of the MCMC algorithm in Prolog, and (c) to provide insights on desirable properties for SLPs.

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References

  • Angelopoulos, N., & Cussens, J. (2001). Markov chain Monte Carlo using tree-based priors on model structure. In Breese, J., & Koller, D. (Eds.), Proc. of the 17th Annual Conf. on Uncertainty in Artificial Intelligence (UAI-2001), pp. 16–23 Seattle. Morgan Kaufmann.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of York, YO10 5DD, Heslington, York, UK

    Nicos Angelopoulos & James Cussens

Authors
  1. Nicos Angelopoulos
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  2. James Cussens
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Editor information

Editors and Affiliations

  1. IF Computer Japan, 5-28-2 Sendagi, Bunkyo-ku, 113-0022, Tokyo, Japan

    Oskar Bartenstein

  2. Fraunhofer FIRST, Kekulé 7, 12489, Berlin, Germany

    Ulrich Geske

  3. think-cell Software GmbH, Invalidenstraße 34, 10115, Berlin, Germany

    Markus Hannebauer

  4. Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka, Japan

    Osamu Yoshie

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© 2003 Springer-Verlag Berlin Heidelberg

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Angelopoulos, N., Cussens, J. (2003). Prolog Issues and Experimental Results of an MCMC Algorithm. In: Bartenstein, O., Geske, U., Hannebauer, M., Yoshie, O. (eds) Web Knowledge Management and Decision Support. INAP 2001. Lecture Notes in Computer Science(), vol 2543. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36524-9_15

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  • DOI: https://doi.org/10.1007/3-540-36524-9_15

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-00680-0

  • Online ISBN: 978-3-540-36524-2

  • eBook Packages: Springer Book Archive

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