Probabilistic programming promises to simplify and democratize probabilistic machine
learning, but successful probabilistic programming systems require flexible, generic and …

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

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

Imagine a world where computational simulations can be inverted as easily as running them
forwards, where data can be used to refine models automatically, and where the only …

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 present an architectural design of a library for Bayesian modelling and inference in
modern functional programming languages. The novel aspect of our approach are modular …

Probabilistic programming has emerged as a powerful paradigm in statistics, applied
science, and machine learning: by decoupling modelling from inference, it promises to allow …

We present a new approach to the design and implementation of probabilistic programming
languages (PPLs), based on the idea of stochastically estimating the probability density …

The posterior in probabilistic programs with stochastic support decomposes as a weighted
sum of the local posterior distributions associated with each possible program path. We …

We present the first general purpose framework for marginal maximum a posteriori
estimation of probabilistic program variables. By using a series of code transformations, the …