Modular construction of Bayesian inference algorithms
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 how it simplifies implementation of Monte Carlo algorithms. Our technique is based on a method for modular construction of interpreters using monad transformers and is applicable generically to probabilistic programming.