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Main Author: Parzygnat, Arthur J.
Format: Preprint
Published: 2024
Subjects:
Online Access:https://arxiv.org/abs/2401.17447
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author Parzygnat, Arthur J.
author_facet Parzygnat, Arthur J.
contents In statistical inference, retrodiction is the act of inferring potential causes in the past based on knowledge of the effects in the present and the dynamics leading to the present. Retrodiction is applicable even when the dynamics is not reversible, and it agrees with the reverse dynamics when it exists, so that retrodiction may be viewed as an extension of inversion, i.e., time-reversal. Recently, an axiomatic definition of retrodiction has been made in a way that is applicable to both classical and quantum probability using ideas from category theory. Almost simultaneously, a framework for information flow in in terms of semicartesian categories has been proposed in the setting of categorical probability theory. Here, we formulate a general definition of retrodiction to add to the information flow axioms in semicartesian categories, thus providing an abstract framework for retrodiction beyond classical and quantum probability theory. More precisely, we extend Bayesian inference, and more generally Jeffrey's probability kinematics, to arbitrary semicartesian categories.
format Preprint
id arxiv_https___arxiv_org_abs_2401_17447
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Reversing information flow: retrodiction in semicartesian categories
Parzygnat, Arthur J.
Category Theory
Information Theory
Probability
Quantum Physics
In statistical inference, retrodiction is the act of inferring potential causes in the past based on knowledge of the effects in the present and the dynamics leading to the present. Retrodiction is applicable even when the dynamics is not reversible, and it agrees with the reverse dynamics when it exists, so that retrodiction may be viewed as an extension of inversion, i.e., time-reversal. Recently, an axiomatic definition of retrodiction has been made in a way that is applicable to both classical and quantum probability using ideas from category theory. Almost simultaneously, a framework for information flow in in terms of semicartesian categories has been proposed in the setting of categorical probability theory. Here, we formulate a general definition of retrodiction to add to the information flow axioms in semicartesian categories, thus providing an abstract framework for retrodiction beyond classical and quantum probability theory. More precisely, we extend Bayesian inference, and more generally Jeffrey's probability kinematics, to arbitrary semicartesian categories.
title Reversing information flow: retrodiction in semicartesian categories
topic Category Theory
Information Theory
Probability
Quantum Physics
url https://arxiv.org/abs/2401.17447