Sufficient Statistics and Split Idempotents in Discrete Probability Theory

Abstract

A sufficient statistic is a deterministic function that captures an essential property of a probabilistic function (channel, kernel). Being a sufficient statistic can be expressed nicely in terms of string diagrams, as Tobias Fritz showed recently, in adjoint form. This reformulation highlights the role of split idempotents, in the Fisher-Neyman factorisation theorem. Examples of a sufficient statistic occur in the literature, but mostly in continuous probability. This paper demonstrates that there are also several fundamental examples of a sufficient statistic in discrete probability. They emerge after some combinatorial groundwork that reveals the relevant dagger split idempotents and shows that a sufficient statistic is a deterministic dagger epi.

References in corpus (6)

• A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics
• Disintegration and Bayesian Inversion via String Diagrams
• Quantum Logic in Dagger Kernel Categories
• Representable Markov Categories and Comparison of Statistical Experiments in Categorical Probability
• Dagger and Dilation in the Category of Von Neumann algebras
• Urns & Tubes

Cited by in corpus (1)

• Markov Categories and Entropy