A Type Theory for Probabilistic and Bayesian Reasoning
Paper in proceedings, 2018

This paper introduces a novel type theory and logic for probabilistic reasoning. Its logic is quantitative, with fuzzy predicates. It includes normalisation and conditioning of states. This conditioning uses a key aspect that distinguishes our probabilistic type theory from quantum type theory, namely the bijective correspondence between predicates and side-effect free actions (called instrument, or assert, maps). The paper shows how suitable computation rules can be derived from this predicate-action correspondence, and uses these rules for calculating conditional probabilities in two well-known examples of Bayesian reasoning in (graphical) models. Our type theory may thus form the basis for a mechanisation of Bayesian inference.

Probabilistic programming

Bayesian reasoning

effect module

probabilistic algorithm

type theory

Author

Bart Jacobs

Radboud University

Robin Adams

Chalmers, Computer Science and Engineering (Chalmers), Computing Science (Chalmers)

Leibniz International Proceedings in Informatics (LIPIcs)

1868-8969 (ISSN)

Vol. 69 1 1-34

21st International Conference on Types for Proofs and Programs, TYPES 2015
Talinn, Estonia,

Subject Categories

Algebra and Logic

Probability Theory and Statistics

Computer Science

Areas of Advance

Information and Communication Technology

Roots

Basic sciences

DOI

10.4230/lipics.types.2015.1

More information

Created

8/19/2018