An Introduction to Probabilistic Programming

TL;DR

This book provides a comprehensive graduate-level introduction to probabilistic programming, covering foundational concepts, inference algorithms, higher-order languages, and the integration of differentiable programming with neural networks.

Contribution

It introduces the design and implementation of both first-order and higher-order probabilistic programming languages, including inference techniques and their applications in neural network models.

Findings

Introduces a first-order probabilistic programming language with graphical model semantics.

Discusses algorithms for inference in higher-order probabilistic languages.

Explores the intersection of probabilistic programming and differentiable programming, including Hamiltonian Monte Carlo and neural network parameterization.

Abstract

This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed to design and build these systems. It is aimed at people who have an undergraduate-level understanding of either or, ideally, both probabilistic machine learning and programming languages. We start with a discussion of model-based reasoning and explain why conditioning is a foundational computation central to the fields of probabilistic machine learning and artificial intelligence. We then introduce a first-order probabilistic programming language (PPL) whose programs correspond to graphical models with a known, finite, set of random variables. In the context of this PPL we introduce fundamental inference algorithms and describe how they can be implemented. We then turn to higher-order probabilistic programming languages. Programs in such languages can define models with dynamic computation graphs, which may not instantiate the same set of random variables in each execution. Inference requires methods that generate samples by repeatedly evaluating the program. Foundational algorithms for this kind of language are discussed in the context of an interface between program executions and an inference controller. Finally we consider the intersection of probabilistic and differentiable programming. We begin with a discussion of automatic differentiation, and how it can be used to implement efficient inference methods based on Hamiltonian Monte Carlo. We then discuss gradient-based maximum likelihood estimation in programs that are parameterized using neural networks, how to amortize inference using by learning neural approximations to the program posterior, and how language features impact the design of deep probabilistic programming systems.