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Probabilistic programming languages and modeling toolkits are two modularways to build and reuse stochastic models and inference procedures. Combiningstrengths of both, we express models and inference as generalized coroutines inthe same general-purpose language. We use existing facilities of the language,such as rich libraries, optimizing compilers, and types, to develop concise,declarative, and realistic models with competitive performance on exact andapproximate inference. In particular, a wide range of models can be expressedusing memoization. Because deterministic parts of models run at full speed,custom inference procedures are trivial to incorporate, and inferenceprocedures can reason about themselves without interpretive overhead. Withinthis framework, we introduce a new, general algorithm for importance samplingwith look-ahead.

Monolingual Probabilistic Programming Using Generalized Coroutines