  We develop a technique for generalising from data in which models are
samplers represented as program text. We establish encouraging empirical
results that suggest that Markov chain Monte Carlo probabilistic programming
inference techniques coupled with higher-order probabilistic programming
languages are now sufficiently powerful to enable successful inference of this
kind in nontrivial domains. We also introduce a new notion of probabilistic
program compilation and show how the same machinery might be used in the future
to compile probabilistic programs for efficient reusable predictive inference.
