We propose Edward, a Turing-complete probabilistic programming language. Edward
defines two compositional representations---random variables and inference. By treating …

We propose and evaluate a framework for creating and running approximation-enabled
MapReduce programs. Specifically, we propose approximation mechanisms that fit naturally …

Abstract We describe Bayesian Layers, a module designed for fast experimentation with
neural network uncertainty. It extends neural network libraries with drop-in replacements for …

Plotkin and Pretnar's handlers for algebraic effects occupy a sweet spot in the design space
of abstractions for effectful computation. By separating effect signatures from their …

We describe a general method of transforming arbitrary programming languages into
probabilistic programming languages with straightforward MCMC inference engines …

We present Hakaru, a new probabilistic programming system that allows composable reuse
of distributions, queries, and inference algorithms, all expressed in a single language of …

Random variables and their distributions are a central part in many areas of statistical
methods. The Distributions. jl package provides Julia users and developers tools for working …

We present a new algorithm for approximate inference in probabilistic programs, based on a
stochastic gradient for variational programs. This method is efficient without restrictions on …

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

A proposal for a formal model, Fragment Grammars, that treats productivity and reuse as the
target of inference in a probabilistic framework. Language allows us to express and …