Zanasi, F;
Jacobs, B;
(2016)
A predicate/state transformer semantics for Bayesian learning.
Electronic Notes in Theoretical Computer Science
, 325
pp. 185-200.
10.1016/j.entcs.2016.09.038.
Preview |
Text
Zanasi_S1571066116300883-main.pdf - Published Version Download (326kB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | A predicate/state transformer semantics for Bayesian learning |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.entcs.2016.09.038 |
| Publisher version: | https://doi.org/10.1016/j.entcs.2016.09.038 |
| Language: | English |
| Additional information: | Copyright © 2016 The Author(s). Published by Elsevier B.V. www.elsevier.com/locate/entcs http://dx.doi.org/10.1016/j.entcs.2016.09.038 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Inference, learning, Bayes Kleisli category, effectus, predicate transformer, state transformer |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1561445 |
Archive Staff Only
 |
View Item |
