The geometry of Bayesian programming
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Abstract We give two geometry of interaction models for a typed λ-calculus with recursion endowed with operators for sampling from a continuous uniform distribution and soft conditioning, namely a paradigmatic calculus for higher-order Bayesian programming. The models are based on the category of measurable spaces and partial measurable functions, and the category of measurable spaces and s-finite kernels, respectively. The former is proved adequate with respect to both a distribution-based and a sampling-based operational semantics, while the latter is proved adequate with respect to a sampling-based operational semantics.
2018 ◽
Vol 2
(POPL)
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pp. 1-28
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1994 ◽
Vol 4
(3)
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pp. 285-335
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2018 ◽
Vol 28
(9)
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pp. 1606-1638
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2013 ◽
Vol 93
(107)
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pp. 1-18
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1996 ◽
Vol 6
(5)
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pp. 409-453
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