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Prolog Issues and Experimental Results of an MCMC Algorithm

  • Nicos Angelopoulos
  • James Cussens
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2543)

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.

Keywords

Markov Chain Markov Chain Monte Carlo Hard Constraint Markov Chain Monte Carlo Algorithm Inductive Logic Programming 
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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References

  1. 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.Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Nicos Angelopoulos
    • 1
  • James Cussens
    • 1
  1. 1.Department of Computer ScienceUniversity of YorkHeslingtonUK

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