Interacting Particle Inferencefor Probabilistic Programming in Haskell

Sammanfattning: Probabilistic programming shows much promise as a declarative way to define statistical models, but inference is often expensive. A parallelisable particle Markovchain Monte Carlo sampler is implemented in Haskell and the domain-specific language Monad-Bayes. The method shows good performance compared to a single SMC sampler, but the full potential of the method could not be acheived.