Program Analysis of Probabilistic Programs
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Probabilistic ProgrammingSimilar Papers 제목 키워드 기반
Static Analysis for Probabilistic Programs
Probabilistic programming is a powerful abstraction for statistical machine learning. Applying static analysis methods to probabilistic programs could serve to optimize the learning process, automatically verify properti…
BIG-bench Machine LearningProbabilistic ProgrammingTransforming Probabilistic Programs for Model Checking
Probabilistic programming is perfectly suited to reliable and transparent data science, as it allows the user to specify their models in a high-level language without worrying about the complexities of how to fit the mod…
modelProbabilistic ProgrammingBayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that cap…
Bayesian InferenceProbabilistic ProgrammingTime SeriesTime Series AnalysisStochastic Probabilistic Programs
We introduce the notion of a stochastic probabilistic program and present a reference implementation of a probabilistic programming facility supporting specification of stochastic probabilistic programs and inference in …
Probabilistic ProgrammingSound Abstraction and Decomposition of Probabilistic Programs
Probabilistic programming languages are a flexible tool for specifying statistical models, but this flexibility comes at the cost of efficient analysis. It is currently difficult to compactly represent the subtle in…
Probabilistic Programming