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)


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.


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  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
  2. Gilks, W. R., Richardson, S., & Spiegelhlater, D. J}. (Eds.). (1996). Markov Chain Monte Carlo in Practise. Chapman & Hall.Google Scholar
  3. Kameya, Y., & Sato, T. (2000). Effficient EM learning with tabulation for parameterized logic programs. In Proc. of the First International Conf. on Computational Logic (CL 2000), Vol. 1861 of LNAI, pp. 269–284 London. Springer.Google Scholar
  4. Muggleton, S. (1996). Stochastic logic programs. In Advances in ILP, Vol. 32 of Frontiers in AI and Applications, pp. 254–264. IOS Press.MathSciNetGoogle Scholar
  5. Pfeffer, A. (2001). IBAL: A probablistic rational programming language. In Proc. of the Seventeenth International Joint Conference on AI (IJCAI-01).Google Scholar
  6. Riezler, S. (1998). Probabilistic Constraint Logic Programming. Ph.D. thesis, Universität Tübingen. AIMS Report 5(1), 1999, IMS, Universität Stuttgart.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

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

Personalised recommendations