Measurable cones and stable, measurable functions: a model for probabilistic higher-order programming
We define a notion of stable and measurable map between cones endowed with measurability tests and show that it forms a cpo-enriched cartesian closed category. This category gives a denotational model of an extension of PCF supporting the main primitives of probabilistic functional programming, like...
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| Veröffentlicht in: | Proceedings of ACM on programming languages Jg. 2; H. POPL; S. 1 - 28 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
01.01.2018
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| ISSN: | 2475-1421, 2475-1421 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | We define a notion of stable and measurable map between cones endowed with measurability tests and show that it forms a cpo-enriched cartesian closed category. This category gives a denotational model of an extension of PCF supporting the main primitives of probabilistic functional programming, like continuous and discrete probabilistic distributions, sampling, conditioning and full recursion. We prove the soundness and adequacy of this model with respect to a call-by-name operational semantics and give some examples of its denotations. |
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| ISSN: | 2475-1421 2475-1421 |
| DOI: | 10.1145/3158147 |