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Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs
We introduce a novel sampling algorithm for Bayesian inference on imperative
probabilistic programs. It features a hierarchical architecture that separates
control flows from data: the top-level samples a control flow, and the bottom
level samples data values along the control flow picked by the top level. This
separation allows us to plug various language-based analysis techniques in
probabilistic program sampling; specifically, we use logical backward
propagation of observations for sampling efficiency. We implemented our
algorithm on top of Anglican. The experimental results demonstrate our
algorithm's efficiency, especially for programs with while loops and rare
observations.Comment: 11 pages with appendice