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A Comparison of MCMC Sampling for Probabilistic Logic Programming

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AI*IA 2019 – Advances in Artificial Intelligence (AI*IA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11946))

Abstract

Markov Chain Monte Carlo (MCMC) methods are a class of algorithms used to perform approximate inference in probabilistic models. When direct sampling from a probability distribution is difficult, MCMC algorithms provide accurate results by constructing a Markov chain that gradually approximates the desired distribution. In this paper we describe and compare the performances of two MCMC sampling algorithms, Gibbs sampling and Metropolis Hastings sampling, with rejection sampling for probabilistic logic programs. In particular, we analyse the relation between execution time and number of samples and how fast each algorithm converges.

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Notes

  1. 1.

    http://www.fe.infn.it/coka/doku.php?id=start.

  2. 2.

    http://cplint.eu/example/inference/arithm.pl.

  3. 3.

    http://cplint.eu/example/inference/hmm.pl.

  4. 4.

    http://cplint.eu/example/inference/lda.swinb.

  5. 5.

    http://cplint.eu/example/inference/uwcse.pl.

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Author information

Authors and Affiliations

  1. Dipartimento di Ingegneria, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy

    Damiano Azzolini & Evelina Lamma

  2. Dipartimento di Matematica e Informatica, University of Ferrara, Via Saragat 1, 44122, Ferrara, Italy

    Fabrizio Riguzzi & Franco Masotti

Authors
  1. Damiano Azzolini
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  2. Fabrizio Riguzzi
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  3. Franco Masotti
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  4. Evelina Lamma
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Corresponding author

Correspondence to Damiano Azzolini .

Editor information

Editors and Affiliations

  1. University of Calabria, Rende, Italy

    Mario Alviano

  2. University of Calabria, Rende, Italy

    Gianluigi Greco

  3. University of Calabria, Rende, Italy

    Francesco Scarcello

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Azzolini, D., Riguzzi, F., Masotti, F., Lamma, E. (2019). A Comparison of MCMC Sampling for Probabilistic Logic Programming. In: Alviano, M., Greco, G., Scarcello, F. (eds) AI*IA 2019 – Advances in Artificial Intelligence. AI*IA 2019. Lecture Notes in Computer Science(), vol 11946. Springer, Cham. https://doi.org/10.1007/978-3-030-35166-3_2

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  • DOI: https://doi.org/10.1007/978-3-030-35166-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35165-6

  • Online ISBN: 978-3-030-35166-3

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