A key problem in statistical modeling is model selection, that is, how to choose a model at an
appropriate level of complexity. This problem appears in many settings, most prominently in …

Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that
parameters are endowed with distributions which may themselves introduce new …

Deep generative models have been wildly successful at learning coherent latent
representations for continuous data such as natural images, artwork, and audio. However …

An investigation of how children balance rules and exceptions when they learn languages.
All languages have exceptions alongside overarching rules and regularities. How does a …

Much research in cognitive neuroscience supports prediction as a canonical computation of
cognition across domains. Is such predictive coding implemented by feedback from higher …

To tackle a hard problem, it is often wise to reuse and recombine existing knowledge. Such
an ability to bootstrap enables us to grow rich mental concepts despite limited cognitive …

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 …

What are the units of text that we want to model? From bytes to multi-word expressions, text
can be analyzed and generated at many granularities. Until recently, most natural language …

We introduce Church, a universal language for describing stochastic generative processes.
Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its …

We present the nested Chinese restaurant process (nCRP), a stochastic process that
assigns probability distributions to ensembles of infinitely deep, infinitely branching trees …