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 …

Recent progress in artificial intelligence has renewed interest in building systems that learn
and think like people. Many advances have come from using deep neural networks trained …

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 …

How does language inform our downstream thinking? In particular, how do humans make
meaning from language--and how can we leverage a theory of linguistic meaning to build …

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

Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines
it according to her analysis, and repeats. However, fitting complex models to large data is a …

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

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 …

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 …

Variational inference is a scalable technique for approximate Bayesian inference. Deriving
variational inference algorithms requires tedious model-specific calculations; this makes it …