Bhat, Sooraj and Borgström, Johannes and Gordon, Andrew D. and Russo, Claudio - Deriving Probability Density Functions from Probabilistic Functional Programs

lmcs:3758 - Logical Methods in Computer Science, July 3, 2017, Volume 13, Issue 2
Deriving Probability Density Functions from Probabilistic Functional Programs

Authors: Bhat, Sooraj and Borgström, Johannes and Gordon, Andrew D. and Russo, Claudio

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework.


Source : oai:arXiv.org:1704.00917
DOI : 10.23638/LMCS-13(2:16)2017
Volume: Volume 13, Issue 2
Published on: July 3, 2017
Submitted on: July 3, 2017
Keywords: Computer Science - Programming Languages,Computer Science - Artificial Intelligence,F.3.2,G.3,I.2.5


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