Mario Román: Partial Markov Categories @ToposInstitute
Mario Román: Partial Markov Categories  @ToposInstitute
Uploaded November 2024 | Updated September 2026, 2 weeks ago
Topos Institute Colloquium, 31st of October 2024.
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Partial Markov categories are an algebra and syntax for Bayesian inference. They use a string diagrammatic syntax—with a formal correspondence to programs—to reason about continuous and discrete probability, decision problems (Monty Hall, Newcomb's), the compositional properties of normalization, and an abstract Bayes' theorem.

Partial Markov categories are a careful blend of Markov categories (from categorical probability theory) and cartesian restriction categories (from the algebraic theory of partial computations). We will discuss the construction, theory, and applications of partial Markov categories.

This is joint work with Elena Di Lavore. It is based on "Evidential Decision Theory via Partial Markov Categories", presented at LiCS'23 (arxiv.org/abs/2301.12989).
Mario Román: Partial Markov Categories[TopOx] Steve Awodey: Path Types in Algebraic Type Theory[DOTS Lectures] 17. Representability for double operad algebras[DOTS Lectures] 3. Moore machines[2-torial] Toposes: from topological spaces to databases[Berkeley Seminar] Evan Patterson | A uniform type theory for internal languages of cat. structures[Oxford Seminar] Matteo Capucci | 2-classifiers for 2-algebrasNina Otter: (Co)algebraic analysis of social systems: from graphs to hypergraphsDan Ghica: Designing and developing an industrial-strength programming language[Oxford Seminar] Nathan Haydon | Peirce’s Existential Graphs[Berkeley Seminar] Hugo Paquet | Lazy categorical semantics of discrete probabilistic programmingChristoph Benzmueller: Many Logics, One Methodology
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Mario Román: "Partial Markov Categories"

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