Deriving Probability Density Functions from Probabilistic Functional Programs

  • Sooraj Bhat
  • Johannes Borgström
  • Andrew D. Gordon
  • Claudio Russo
Conference paper

DOI: 10.1007/978-3-642-36742-7_35

Volume 7795 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Bhat S., Borgström J., Gordon A.D., Russo C. (2013) Deriving Probability Density Functions from Probabilistic Functional Programs. In: Piterman N., Smolka S.A. (eds) Tools and Algorithms for the Construction and Analysis of Systems. TACAS 2013. Lecture Notes in Computer Science, vol 7795. Springer, Berlin, Heidelberg

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sooraj Bhat
    • 1
  • Johannes Borgström
    • 2
  • Andrew D. Gordon
    • 3
  • Claudio Russo
    • 3
  1. 1.Georgia Institute of TechnologyUSA
  2. 2.Uppsala UniversitySweden
  3. 3.Microsoft ResearchUK