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CATEGORICAL LOGIC

MACHINE LEARNING

## A Predicate/State Transformer Semantics for Bayesian Learning

Resource type

Authors/contributors

- Jacobs, Bart (Author)
- Zanasi, Fabio (Author)

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

MACHINE LEARNING

PROBABILITY & STATISTICS

PROGRAMMING LANGUAGES

Topic

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