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ICDT
2016
ACM

Declarative Probabilistic Programming with Datalog

3 years 14 days ago
Declarative Probabilistic Programming with Datalog
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 ...
Vince Bárány, Balder ten Cate, Benny
Added 04 Apr 2016
Updated 04 Apr 2016
Type Journal
Year 2016
Where ICDT
Authors Vince Bárány, Balder ten Cate, Benny Kimelfeld, Dan Olteanu, Zografoula Vagena
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