  Most of Markov Chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC)
algorithms in existing probabilistic programming systems suboptimally use only
model priors as proposal distributions. In this work, we describe an approach
for training a discriminative model, namely a neural network, in order to
approximate the optimal proposal by using posterior estimates from previous
runs of inference. We show an example that incorporates a data-driven proposal
for use in a non-parametric model in the Anglican probabilistic programming
system. Our results show that data-driven proposals can significantly improve
inference performance so that considerably fewer particles are necessary to
perform a good posterior estimation.
