Abstract:
This dissertation defends the thesis that novel and useful domain-specific languages for solving statistical problems can be embedded in statically-typed, purelyfunctional programming languages. It presents techniques for representing probability distributions in embedded languages, deeply-embedding a type-safe probabilistic programming language in a way that is amenable to inference, and embedding a language for building composite Markov transition operators that can be used in MCMC.
