一句话：提出一种概率函数式语言的密度编译器，并为含失败及离散/连续分布的程序证明其可靠性。
概率密度函数是概率论与机器学习推断的核心概念，但将概率函数式程序编译为密度函数的必要框架直到最近才被提出。本文面向一种带有失败以及离散和连续分布的概率语言，设计密度编译器，并给出该编译器的可靠性证明。该方法把概率程序变换为可计算密度，从而减轻领域专家编写推断模型与求解器的负担。作者利用标准马尔可夫链蒙特卡洛框架，在一系列科学应用推断问题上进行评估，包括全球碳循环建模，表明编译器能够有效求解实际推断问题。
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.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@article{BhatBGR17,
title = {Deriving Probability Density Functions from Probabilistic Functional Programs},
author = {Sooraj Bhat and Johannes Borgström and Andrew D. Gordon and Claudio Russo},
journal = {Logical Methods in Computer Science},
volume = {Volume 13, Issue 2},
year = {2017},
doi = {10.23638/lmcs-13(2:16)2017}
}