  Particle Markov chain Monte Carlo techniques rank among current
state-of-the-art methods for probabilistic program inference. A drawback of
these techniques is that they rely on importance resampling, which results in
degenerate particle trajectories and a low effective sample size for variables
sampled early in a program. We here develop a formalism to adapt ancestor
resampling, a technique that mitigates particle degeneracy, to the
probabilistic programming setting. We present empirical results that
demonstrate nontrivial performance gains.
