  Probabilistic programming is related to a compositional approach to
stochastic modeling by switching from discrete to continuous time dynamics. In
continuous time, an operator-algebra semantics is available in which processes
proceeding in parallel (and possibly interacting) have summed time-evolution
operators. From this foundation, algorithms for simulation, inference and model
reduction may be systematically derived. The useful consequences are
potentially far-reaching in computational science, machine learning and beyond.
Hybrid compositional stochastic modeling/probabilistic programming approaches
may also be possible.
