A Categorical Approach to Probability Theory

By R. Fric, M. Papčo. Studia Logica: An International Journal for Symbolic Logic, 2010

447 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/449a1c309aeb03ce2d1c950ea1c8661fffbe77e5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Hongseok Yang
2. Frank D. Wood
3. S. Staton
4. Chris Heunen
5. Ohad Kammar
6. Matthew J. Parkinson
7. Peter W. O'Hearn

In Management Science and Operations Research

1. S. Staton
2. Chris Heunen
3. Frank D. Wood
4. Ohad Kammar
5. Hongseok Yang

Citations in the corpus

• Semantics for probabilistic programming: higher-order functions, continuous distributions, and soft constraints (2016, methodology): “Normalization The command norm(u) runs a simulation algorithm over the program fragment u.”
• Probabilistic Inference by Program Transformation in Hakaru (System Description) (2016, background): “The type constructor HMeasure is a monad [3, 10], whose unit and bind operations are spelled dirac and bind.”
• A convenient category for higher-order probability theory (2017, methodology): “R0190-162011, Development of Vulnerability Discovery Technologies for IoT Software Security).”
• Commutative Semantics for Probabilistic Programming (2017, background): “For example, the monoidal category of probability kernels is the Kleisli category for the Giry monad [10].”