Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs

In the recent rise of statistical machine learning, probabilistic programming languages are attracting a lot attention as a programming infrastructure for data processing tasks. Probabilistic programming frameworks allow users to express statistical models as programs, and offer a variety of methods for analyzing the models. Probabilistic programs feature randomization and conditioning. Randomization can take different forms, such as probabilistic branching (ifp in Program 1) and random assignment from a probability distribution (denoted by, see Program 2).