KR2021Proceedings of the 18th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning

Online event. November 3-12, 2021.

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ISSN: 2334-1033
ISBN: 978-1-956792-99-7

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Copyright © 2021 International Joint Conferences on Artificial Intelligence Organization

Formalizing Statistical Beliefs in Hypothesis Testing Using Program Logic

  1. Yusuke Kawamoto(AIST, Japan, PRESTO, JST, Japan)
  2. Tetsuya Sato(Tokyo Institute of Technology, Japan)
  3. Kohei Suenaga(Kyoto University, Japan)

Keywords

  1. Reasoning about knowledge, beliefs, and other mental attitudes

Abstract

We propose a new approach to formally describing the requirement for statistical inference and checking whether the statistical method is appropriately used in a program. Specifically, we define belief Hoare logic (BHL) for formalizing and reasoning about the statistical beliefs acquired via hypothesis testing. This logic is equipped with axiom schemas for hypothesis tests and rules for multiple tests that can be instantiated to a variety of concrete tests. To the best of our knowledge, this is the first attempt to introduce a program logic with epistemic modal operators that can specify the preconditions for hypothesis tests to be applied appropriately.