Probability Theory from a Programming Perspective (Invited Paper)

Author Sam Staton

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Author Details

Sam Staton
  • Department of Computer Science, University of Oxford, Oxford OX1 3QD UK

Cite AsGet BibTex

Sam Staton. Probability Theory from a Programming Perspective (Invited Paper). In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, p. 3:1, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


A leading idea is to apply techniques from verification and programming theory to machine learning and statistics, to deal with things like compositionality and various notions of correctness and complexity. Probabilistic programming is an example of this. Moreover, this approach leads to new foundational methods in probability theory. This is particularly true in the "non-parametric" aspects, for example in higher-order functions and infinite random graph models.

Subject Classification

ACM Subject Classification
  • Theory of computation → Computational complexity and cryptography
  • correctness
  • complexity
  • statistics


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