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preprint · SPIRE - Sciences Po Institutional REpository · 2019

The Geometry of Bayesian Programming

Ugo Dal Lago, Naohiko Hoshino

Cited by 8Open access

Abstract

We give a geometry of interaction model for a typed λ -calculus endowed with operators for sampling from a continuous uniform distribution and soft conditioning, namely a paradigmatic calculus for higher-order Bayesian programming. The model is based on the category of measurable spaces and partial measurable functions, and is proved adequate with respect to both a distribution-based and a sampling-based operational semantics.

Cite this paper

APA 7

Lago, U. D., & Hoshino, N. (2019). The Geometry of Bayesian Programming. SPIRE - Sciences Po Institutional REpository, 3, 1–13. https://doi.org/10.1109/lics.2019.8785663

MLA 9

Lago, Ugo Dal, and Naohiko Hoshino. “The Geometry of Bayesian Programming.” SPIRE - Sciences Po Institutional REpository, vol. 3, 2019, pp. 1–13, https://doi.org/10.1109/lics.2019.8785663.

Chicago

Lago, Ugo Dal, and Naohiko Hoshino. 2019. “The Geometry of Bayesian Programming.” SPIRE - Sciences Po Institutional REpository 3: 1–13. https://doi.org/10.1109/lics.2019.8785663.

Harvard

Lago, U.D. and Hoshino, N. (2019) ‘The Geometry of Bayesian Programming’, SPIRE - Sciences Po Institutional REpository, 3, pp. 1–13. Available at: https://doi.org/10.1109/lics.2019.8785663.

BibTeX
@misc{lago2019geometry,
  title = {{The Geometry of Bayesian Programming}},
  author = {Lago, Ugo Dal and Hoshino, Naohiko},
  journal = {SPIRE - Sciences Po Institutional REpository},
  volume = {3},
  pages = {1--13},
  year = {2019},
  doi = {10.1109/lics.2019.8785663}
}

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