  We present a meta-algorithm for learning a posterior-inference algorithm for
restricted probabilistic programs. Our meta-algorithm takes a training set of
probabilistic programs that describe models with observations, and attempts to
learn an efficient method for inferring the posterior of a similar program. A
key feature of our approach is the use of what we call a white-box inference
algorithm that extracts information directly from model descriptions
themselves, given as programs in a probabilistic programming language.
Concretely, our white-box inference algorithm is equipped with multiple neural
networks, one for each type of atomic command in the language, and computes an
approximate posterior of a given probabilistic program by analysing individual
atomic commands in the program using these networks. The parameters of these
networks are then learnt from a training set by our meta-algorithm. Our
empirical evaluation for six model classes shows the promise of our approach.
