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

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 …

We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic
programming language that automatically delivers exact solutions to a broad range of …

This paper presents a wp--style calculus for obtaining expectations on the outcomes of
(mutually) recursive probabilistic programs. We provide several proof rules to derive one …

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 …

Binary program dependence analysis determines dependence between instructions and
hence is important for many applications that have to deal with executables without any …

The problem of probabilistic modeling and inference, at a high-level, can be viewed as
constructing a (model, query, inference) tuple, where an inference algorithm implements a …

We present PrivInfer, an expressive framework for writing and verifying differentially private
Bayesian machine learning algorithms. Programs in PrivInfer are written in a rich functional …

Probabilistic programming is the idea of writing models from statistics and machine learning
using program notations and reasoning about these models using generic inference …

Inconsistent security policy enforcement within the Android framework can allow malicious
actors to improperly access sensitive resources. A number of prominent inconsistency …