Deriving Probability Density Functions from Probabilistic Functional Programs

Sooraj Bhat, Johannes Borgström, Andrew D. Gordon, Claudio V. Russo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 discrete and continuous distributions, and discrete observations, 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.
Original languageEnglish
Title of host publicationTools and Algorithms for the Construction and Analysis of Systems
Subtitle of host publication19th International Conference, TACAS 2013, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2013, Rome, Italy, March 16-24, 2013. Proceedings
PublisherSpringer Berlin Heidelberg
Pages508-522
Number of pages15
Volume7795
ISBN (Electronic)978-3-642-36742-7
ISBN (Print)978-3-642-36741-0
DOIs
Publication statusPublished - 2013

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