Inference of Probabilistic Programs with Moment-Matching Gaussian Mixtures
Francesca Randone,
Luca Bortolussi,
Emilio Incerto
et al.
Abstract:Computing the posterior distribution of a probabilistic program is a hard task for which no one-fit-for-all solution exists. We propose Gaussian Semantics, which approximates the exact probabilistic semantics of a bounded program by means of Gaussian mixtures. It is parametrized by a map that associates each program location with the moment order to be matched in the approximation. We provide two main contributions. The first is a universal approximation theorem stating that, under mild conditions, Gaussian Se… Show more
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