Probability theory
Lasciate ogne 𝔼[X], voi ch’intrate.
— Inferno, Canto terzo.
Probability theory
It should be said: for someone trained in formal methods, the area of probability theory can be rather sloppy […] We even dare to think that this ‘sloppiness’ is ultimately a hindrance to further development of the field, especially in computer science.
— Bart Jacobs, Structured Probabilistic Reasoning (2025).
By topic.
- Probabilistic programming, and programming syntax.
- Synthetic probability theory, and Markov categories.
- Causality, causal reasoning and interventions.
References.
- A Synthetic Approach to Markov Kernels (Fritz, 2020)
- A Convenient Category for Higher-Order Probability Theory (Heunen et al, 2017)
- Hyper Normalisation and Conditioning for Discrete Probability Distributions (Jacobs, 2016)
- Abstract Hypernormalisation, and Normalisation-by-Trace-Evaluation for Generative Systems (Garner, 2022)
- Probability, Valutations, Hyperspace (Fritz, Perrone, Rezagholi, 2021)
- Partial Markov Categories (Di Lavore, Roman, Sobocinski, 2025)
- Evidential Decision Theory via Partial Markov Categories (Di Lavore, Román, 2023)
- Chance and Mass Interpreations of Probability in Markov Decision Processes (Tsai, Phalakarn, Ashkay, Hasuo, 2025)