Many problems in machine learning and statistics involve nested expectations and thus do
not permit conventional Monte Carlo (MC) estimation. For such problems, one must nest …

Recent developments in computational linguistics offer ways to approach the analysis of
musical structure by inducing probabilistic models (in the form of grammars) over a corpus of …

Probabilities describe degrees of belief, and probabilistic inference describes rational
reasoning under uncertainty. It is no wonder, then, that probabilistic models have exploded …

A wide range of human reasoning patterns can be explained as conditioning in probabilistic
models; however, conditioning has traditionally been viewed as an operation applied to …

Recursive calls over recursive data are useful for generating probability distributions, and
probabilistic programming allows computations over these distributions to be expressed in a …

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 …

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 …

We formalize the notion of nesting probabilistic programming queries and investigate the
resulting statistical implications. We demonstrate that while query nesting allows the …

We propose the use of hyperedge replacement graph grammars for factor graphs, or factor
graph grammars (FGGs) for short. FGGs generate sets of factor graphs and can describe a …