Slice Sampling for Probabilistic Programming
This work addresses the problem of flexible and efficient inference for users of probabilistic programming languages, though it appears incremental as it builds on existing slice sampling techniques.
The authors tackled the challenge of inference in probabilistic programming by introducing the first general-purpose slice sampling engine, which outperforms previous methods in experiments including logistic regression, HMM, and Bayesian Neural Net.
We introduce the first, general purpose, slice sampling inference engine for probabilistic programs. This engine is released as part of StocPy, a new Turing-Complete probabilistic programming language, available as a Python library. We present a transdimensional generalisation of slice sampling which is necessary for the inference engine to work on traces with different numbers of random variables. We show that StocPy compares favourably to other PPLs in terms of flexibility and usability, and that slice sampling can outperform previously introduced inference methods. Our experiments include a logistic regression, HMM, and Bayesian Neural Net.