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 …

Higher-order probabilistic programming languages allow programmers to write
sophisticated models in machine learning and statistics in a succinct and structured way, but …

The notions of disintegration and Bayesian inversion are fundamental in conditional
probability theory. They produce channels, as conditional probabilities, from a joint state, or …

We show that a measure-based denotational semantics for probabilistic programming is
commutative. The idea underlying probabilistic programming languages (Anglican, Church …

We give an adequate denotational semantics for languages with recursive higher-order
types, continuous probability distributions, and soft constraints. These are expressive …

Metareasoning can be achieved in probabilistic programming languages (PPLs) using
agent models that recursively nest inference queries inside inference queries. However, the …

We develop the operational semantics of an untyped probabilistic λ-calculus with continuous
distributions, and both hard and soft constraints, as a foundation for universal probabilistic …

Anglican is a probabilistic programming system designed to interoperate with Clojure and
other JVM languages. We introduce the programming language Anglican, outline our design …

We define a notion of stable and measurable map between cones endowed with
measurability tests and show that it forms a cpo-enriched cartesian closed category. This …

Probabilistic programming languages (PPLs) are receiving widespread attention for
performing Bayesian inference in complex generative models. However, applications to …