The machine learning community has recently shown a lot of interest in practical probabilistic programming systems that target the problem of Bayesian inference. Such systems come in different forms,… (More)

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

We propose a set of abstractions to modularize implementation of Bayesian inference algorithms. We provide a proof-of-concept implementation as a Haskell library and demonstrate on several examples… (More)

We provide a theoretical foundation for non-parametric estimation of functions of random variables using kernel mean embeddings. We show that for any continuous function f , consistent estimators of… (More)

Regression formulas are a domain-specific language adopted by several R packages for describing an important and useful class of statistical models: hierarchical linear regressions. Formulas are… (More)

The machine learning community has recently shown a lot of interest in practical probabilistic programming systems that target the problem of Bayesian inference. Such systems come in different forms,… (More)

Recently there has been a lot of interest in the machine learning community in expressing Bayesian models as probabilistic programs in order to make them more reusable and compositional. Such… (More)

We propose denotational semantics for a language of probabilistic arithmetic expressions based on reproducing kernel Hilbert spaces (RKHS). The RKHS approach has numerous practical advantages, but… (More)

Models of noninteracting fermions coupled to auxiliary classical fields are relevant to the understanding of a wide variety of problems in many-body physics, e.g., the description of manganites,… (More)