Program Analysis of Probabilistic Programs
This work addresses the challenge of making statistical analysis more accessible for users of probabilistic programming languages, though it is incremental in improving existing methods.
The paper tackles the problem of inefficient inference in probabilistic programming by developing three novel program analysis techniques that adapt programs to improve inference efficiency, achieving results that are tedious or impossible manually.
Probabilistic programming is a growing area that strives to make statistical analysis more accessible, by separating probabilistic modelling from probabilistic inference. In practice this decoupling is difficult. No single inference algorithm can be used as a probabilistic programming back-end that is simultaneously reliable, efficient, black-box, and general. Probabilistic programming languages often choose a single algorithm to apply to a given problem, thus inheriting its limitations. While substantial work has been done both to formalise probabilistic programming and to improve efficiency of inference, there has been little work that makes use of the available program structure, by formally analysing it, to better utilise the underlying inference algorithm. This dissertation presents three novel techniques (both static and dynamic), which aim to improve probabilistic programming using program analysis. The techniques analyse a probabilistic program and adapt it to make inference more efficient, sometimes in a way that would have been tedious or impossible to do by hand.