{"docId":3758,"paperId":3758,"url":"https:\/\/lmcs.episciences.org\/3758","doi":"10.23638\/LMCS-13(2:16)2017","journalName":"Logical Methods in Computer Science","issn":"","eissn":"1860-5974","volume":[{"vid":305,"name":"Volume 13, Issue 2"}],"section":[],"repositoryName":"arXiv","repositoryIdentifier":"1704.00917","repositoryVersion":2,"repositoryLink":"https:\/\/arxiv.org\/abs\/1704.00917v2","dateSubmitted":"2017-07-03 09:47:24","dateAccepted":"2017-07-03 09:55:48","datePublished":"2017-07-03 09:56:27","titles":["Deriving Probability Density Functions from Probabilistic Functional Programs"],"authors":["Bhat, Sooraj","Borgstr\u00f6m, Johannes","Gordon, Andrew D.","Russo, Claudio"],"abstracts":["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."],"keywords":["Computer Science - Programming Languages","Computer Science - Artificial Intelligence","F.3.2","G.3","I.2.5"]}