Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages
Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages
Probabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic programming languages (PPLs) employ different inference algorithms, such as sequential Monte Carlo (SMC), Markov chain Monte Carlo (MCMC), or variational methods. Existing research on such algorithms mainly …