  Many practical techniques for probabilistic inference require a sequence of
distributions that interpolate between a tractable distribution and an
intractable distribution of interest. Usually, the sequences used are simple,
e.g., based on geometric averages between distributions. When models are
expressed as probabilistic programs, the models themselves are highly
structured objects that can be used to derive annealing sequences that are more
sensitive to domain structure. We propose an algorithm for transforming
probabilistic programs to coarse-to-fine programs which have the same marginal
distribution as the original programs, but generate the data at increasing
levels of detail, from coarse to fine. We apply this algorithm to an Ising
model, its depth-from-disparity variation, and a factorial hidden Markov model.
We show preliminary evidence that the use of coarse-to-fine models can make
existing generic inference algorithms more efficient.
