A Predicate/State Transformer Semantics for Bayesian Learning

Resource type
Authors/contributors
Title
A Predicate/State Transformer Semantics for Bayesian Learning
Abstract
This paper establishes a link between Bayesian inference (learning) and predicate and state transformer operations from programming semantics and logic. Specifically, a very general definition of backward inference is given via first applying a predicate transformer and then conditioning. Analogously, forward inference involves first conditioning and then applying a state transformer. These definitions are illustrated in many examples in discrete and continuous probability theory and also in quantum theory.
Publication
Electronic Notes in Theoretical Computer Science
Volume
325
Pages
185-200
Date
October 5, 2016
Series
The Thirty-second Conference on the Mathematical Foundations of Programming Semantics (MFPS XXXII)
Journal Abbr
Electronic Notes in Theoretical Computer Science
Language
en
DOI
10/ggdgbb
ISSN
1571-0661
Accessed
2019-11-24T12:04:12Z
Library Catalog
ScienceDirect
Extra
ZSCC: 0000030
Citation
Jacobs, B., & Zanasi, F. (2016). A Predicate/State Transformer Semantics for Bayesian Learning. Electronic Notes in Theoretical Computer Science, 325, 185–200. https://doi.org/10/ggdgbb
CATEGORICAL LOGIC
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