Interacting Particle Inferencefor Probabilistic Programming in Haskell

University essay from Uppsala universitet/Institutionen för informationsteknologi

Author: Per Engström; [2018]

Keywords: ;

Abstract: 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.

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