Scaling exact inference for discrete probabilistic programs

Probabilistic programming languages (PPLs) are an expressive means of representing and reasoning about probabilistic models. The computational challenge of probabilistic inference remains the primary roadblock for applying PPLs in practice. Inference is fundamentally hard, so there is no one-size-fi...

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Vydané v:	Proceedings of ACM on programming languages Ročník 4; číslo OOPSLA; s. 1 - 31
Hlavní autori:	Holtzen, Steven, Van den Broeck, Guy, Millstein, Todd
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, NY, USA ACM 13.11.2020
Predmet:	Mathematics of computing Probabilistic inference problems Probabilistic representations Probability and statistics Probabilistic programming
ISSN:	2475-1421, 2475-1421
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