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
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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