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

lmcs:3758 - Logical Methods in Computer Science, July 3, 2017, Volume 13, Issue 2 - https://doi.org/10.23638/LMCS-13(2:16)2017
Deriving Probability Density Functions from Probabilistic Functional Programs

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

    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.


    Volume: Volume 13, Issue 2
    Published on: July 3, 2017
    Accepted 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

    Linked data

    Source : ScholeXplorer References ARXIV 1308.0689
    Source : ScholeXplorer References DOI 10.1007/978-3-642-19718-5_5
    Source : ScholeXplorer References DOI 10.2168/lmcs-9(3:11)2013
    Source : ScholeXplorer References DOI 10.48550/arxiv.1308.0689
    Source : ScholeXplorer References HANDLE 20.500.11820/f455bcf6-6047-4ef6-9302-5ad4befa69b5
    • 20.500.11820/f455bcf6-6047-4ef6-9302-5ad4befa69b5
    • 1308.0689
    • 10.48550/arxiv.1308.0689
    • 10.2168/lmcs-9(3:11)2013
    • 10.2168/lmcs-9(3:11)2013
    • 10.1007/978-3-642-19718-5_5
    • 10.1007/978-3-642-19718-5_5
    • 10.1007/978-3-642-19718-5_5
    Measure transformer semantics for Bayesian machine learning
    Michael Greenberg ; Jurgen Van Gael ; Johannes Borgström ; Andrew D. Gordon ; James Margetson ;

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