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Declarative probabilistic programming with Datalog. (English) Zbl 1369.68119
Martens, Wim (ed.) et al., 19th international conference on database theory, ICDT ’16, Bordeaux, France, March 15–18, 2016. Proceedings. Wadern: Schloss Dagstuhl – Leibniz Zentrum für Informatik (ISBN 978-3-95977-002-6). LIPIcs – Leibniz International Proceedings in Informatics 48, Article 7, 19 p. (2016).
Summary: Probabilistic programming languages are used for developing statistical models, and they typically consist of two components: a specification of a stochastic process (the prior), and a specification of observations that restrict the probability space to a conditional subspace (the posterior). Use cases of such formalisms include the development of algorithms in machine learning and artificial intelligence. We propose and investigate an extension of Datalog for specifying statistical models, and establish a declarative probabilistic-programming paradigm over databases. Our proposed extension provides convenient mechanisms to include common numerical probability functions; in particular, conclusions of rules may contain values drawn from such functions. The semantics of a program is a probability distribution over the possible outcomes of the input database with respect to the program. Observations are naturally incorporated by means of integrity constraints over the extensional and intensional relations. The resulting semantics is robust under different chases and invariant to rewritings that preserve logical equivalence.
For the entire collection see [Zbl 1338.68008].
MSC:
68N19 Other programming paradigms (object-oriented, sequential, concurrent, automatic, etc.)
68N17 Logic programming
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