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

We study the semantic foundation of expressive probabilistic programming languages, that
support higher-order functions, continuous distributions, and soft constraints (such as …

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 …

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 …

We present a modular semantic account of Bayesian inference algorithms for probabilistic
programming languages, as used in data science and machine learning. Sophisticated …

Probabilistic programming languages are used to write probabilistic models to make
probabilistic inferences. A number of rigorous semantics have recently been proposed that …

Probability theory and statistics are fundamental disciplines in a data-driven world. Synthetic
probability theory is a general, axiomatic formalism to describe their underlying structures …