  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.
