How can a machine learn from experience? Probabilistic modelling provides a framework
for understanding what learning is, and has therefore emerged as one of the principal …

Pyro is a probabilistic programming language built on Python as a platform for develo**
advanced probabilistic models in AI research. To scale to large data sets and high …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Probabilistic programming allows for automatic Bayesian inference on user-defined
probabilistic models. Recent advances in Markov chain Monte Carlo (MCMC) sampling …

We propose a new probabilistic programming language for the design and analysis of
perception systems, especially those based on machine learning. Specifically, we consider …

This work offers a broad perspective on probabilistic modeling and inference in light of
recent advances in probabilistic programming, in which models are formally expressed in …

Although probabilistic programming is widely used for some restricted classes of statistical
models, existing systems lack the flexibility and efficiency needed for practical use with more …

Probabilistic modeling is a powerful approach for analyzing empirical information. We
describe Edward, a library for probabilistic modeling. Edward's design reflects an iterative …

We present a new semantic parsing model for answering compositional questions on semi-
structured Wikipedia tables. Our parser is an encoder-decoder neural network with two key …

This book is a graduate-level introduction to probabilistic programming. It not only provides a
thorough background for anyone wishing to use a probabilistic programming system, but …