A Comparison of Probabilistic Programming Languages
Assigned to Michael Detlef Joachim Schiller.
The goal of this bachelor thesis was to investigate the differences between probabilistic programming languages. Therefore a case study was performed. We represented a hidden markov model and implemented inference on it with two probabilistic programming languages, WebPPL and Pyro. WebPPL following a functional approach as well as Pyro that sticks to an imperative programming paradigm succeeded in representing the hidden Markov model. Concerning the inference on the hidden markov model critical spots such as dealing with state depending models were revealed. We came to the conclusion that there exists a balancing problem between a generalization and a highly individual implementation of inference algorithms for probabilistic program- ming languages. The bachelor thesis is interesting for Bachelor or Master students of computer science and related courses of studies. It is also in- teresting for anybody dealing with probabilistic models and inference on them.