eng
episciences.org
Logical Methods in Computer Science
1860-5974
2017-07-03
Volume 13, Issue 2
10.23638/LMCS-13(2:16)2017
3758
journal article
Deriving Probability Density Functions from Probabilistic Functional Programs
Sooraj Bhat
Johannes BorgstrÃ¶m
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.
https://lmcs.episciences.org/3758/pdf
Computer Science - Programming Languages
Computer Science - Artificial Intelligence
F.3.2
G.3
I.2.5