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A Type Theory for Probabilistic and Bayesian Reasoning

Authors Robin Adams, Bart Jacobs

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LIPIcs.TYPES.2015.1.pdf
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Keywords
  • Probabilistic programming
  • probabilistic algorithm
  • type theory
  • effect module
  • Bayesian reasoning

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Abstract

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.

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Robin Adams and Bart Jacobs. A Type Theory for Probabilistic and Bayesian Reasoning. In 21st International Conference on Types for Proofs and Programs (TYPES 2015). Leibniz International Proceedings in Informatics (LIPIcs), Volume 69, pp. 1:1-1:34, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.TYPES.2015.1

Author Details

Robin Adams
Bart Jacobs

References

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