  Probabilistic programming is a programming paradigm for expressing flexible
probabilistic models. Implementations of probabilistic programming languages
employ a variety of inference algorithms, where sequential Monte Carlo methods
are commonly used. A problem with current state-of-the-art implementations
using sequential Monte Carlo inference is the alignment of program
synchronization points. We propose a new static analysis approach based on the
0-CFA algorithm for automatically aligning higher-order probabilistic programs.
We evaluate the automatic alignment on a phylogenetic model, showing a
significant decrease in runtime and increase in accuracy.
