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 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 …

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

Everything that happens has a multitude of causes, but people make causal judgments
effortlessly. How do people select one particular cause (eg, the lightning bolt that set the …

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

Wir studieren die quantitative Analyse probabilistischer Programme. Dabei untersuchen wir
vornehmlich zwei Aspekte: Die Analysetechniken selbst, sowie die komplexitäts-bzw …

Probabilities describe degrees of belief, and probabilistic inference describes rational
reasoning under uncertainty. It is no wonder, then, that probabilistic models have exploded …

This article investigates the semantic intricacies of conditioning, a main feature in
probabilistic programming. Our study is based on an extension of the imperative …

Bayesian inference, of posterior knowledge from prior knowledge and observed evidence, is
typically defined by Bayes's rule, which says the posterior multiplied by the probability of an …