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.

Jacobs B., Zanasi F. (2016). A Predicate/State Transformer Semantics for Bayesian Learning. PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS : ELSEVIER SCIENCE BV [10.1016/j.entcs.2016.09.038].

A Predicate/State Transformer Semantics for Bayesian Learning

Zanasi F.
2016

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.
2016
Proceedings of the 32nd Conference on the Mathematical Foundations of Programming Semantics, MFPS 2016
185
200
Jacobs B., Zanasi F. (2016). A Predicate/State Transformer Semantics for Bayesian Learning. PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS : ELSEVIER SCIENCE BV [10.1016/j.entcs.2016.09.038].
Jacobs B.; Zanasi F.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/904987
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