Abstract
Probabilistic logic programming languages are powerful formalisms that can model complex problems where it is necessary to represent both structure and uncertainty. Using exact inference methods to compute conditional probabilities in these languages is often intractable so approximate inference techniques are necessary. This paper proposes a Markov Chain Monte Carlo algorithm for estimating conditional probabilities based on sampling from an AND/OR tree for ProbLog, a general-purpose probabilistic logic programming language. We propose a parameterizable proposal distribution that generates the next sample in the Markov chain by probabilistically traversing the AND/OR tree from its root, which holds the evidence, to the leaves. An empirical evaluation on several different applications illustrates the advantages of our algorithm.
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Moldovan, B., Thon, I., Davis, J., de Raedt, L. (2013). MCMC Estimation of Conditional Probabilities in Probabilistic Programming Languages. In: van der Gaag, L.C. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2013. Lecture Notes in Computer Science(), vol 7958. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39091-3_37
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DOI: https://doi.org/10.1007/978-3-642-39091-3_37
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