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cs.PL2026
Incremental Computation for Efficient Programmable Inference in Probabilistic Programs
Fabian Zaiser, Jack Czenszak, Martin C. Rinard +2
Inference in probabilistic programs generally requires evaluating many possible program executions to find those of high posterior density. To scale inference to large datasets, it…
cs.PL2024
Guaranteed Bounds on Posterior Distributions of Discrete Probabilistic Programs with Loops
Fabian Zaiser, Andrzej S. Murawski, C. -H. Luke Ong
We study the problem of bounding the posterior distribution of discrete probabilistic programs with unbounded support, loops, and conditioning. Loops pose the main difficulty in th…