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Dilations and information flow axioms in categorical probability

Abstract:
We study the positivity and causality axioms for Markov categories as properties of dilations and information flow and also develop variations thereof for arbitrary semicartesian monoidal categories. These help us show that being a positive Markov category is merely an additional property of a symmetric monoidal category (rather than extra structure). We also characterize the positivity of representable Markov categories and prove that causality implies positivity, but not conversely. Finally, we note that positivity fails for quasi-Borel spaces and interpret this failure as a privacy property of probabilistic name generation.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1017/s0960129523000324

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author


Publisher:
Cambridge University Press
Journal:
Mathematical Structures in Computer Science More from this journal
Volume:
33
Issue:
10
Pages:
913-957
Publication date:
2023-10-25
Acceptance date:
2023-09-11
DOI:
EISSN:
1469-8072
ISSN:
0960-1295


Language:
English
Keywords:
Pubs id:
1561173
Local pid:
pubs:1561173
Deposit date:
2024-05-09

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