Optimizing the expected values of probabilistic processes is a central problem in computer
science and its applications, arising in fields ranging from artificial intelligence to operations …

Emerging research in computer graphics, inverse problems, and machine learning requires
us to differentiate and optimize parametric discontinuities. These discontinuities appear in …

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

Distributions on integers are ubiquitous in probabilistic modeling but remain challenging for
many of today's probabilistic programming languages (PPLs). The core challenge comes …

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 …

We show that streams and lazy data structures are a natural idiom for programming with
infinite-dimensional Bayesian methods such as Poisson processes, Gaussian processes …

This article presents a type-based analysis for deriving upper bounds on the expected
execution cost of probabilistic programs. The analysis is naturally compositional, parametric …

Compared to the wide array of advanced Monte Carlo methods supported by modern
probabilistic programming languages (PPLs), PPL support for variational inference (VI) is …

A key design constraint when implementing Monte Carlo and variational inference
algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities …

We introduce a new setting, the category of ωPAP spaces, for reasoning denotationally
about expressive differentiable and probabilistic programming languages. Our semantics is …