Paper Citation Record · LEDGER

A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics

As of 20 August 2026, this Paper Citation Record lists 100 of 118 outbound references and 5 inbound Pith citation observations for arXiv:1908.07021.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1908.07021 v8

Coverage vector

measured 100 of 118 reference resolution

Typed states for the displayed outbound observations.

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measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: pith, observed 2026-07-11T22:28:20.029484Z

Reference resolution

100 of 118 outbound references displayed

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External citation measurements

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Outbound references

Observation 78ca78c9-393f-44a3-9755-0d9c5710f870 · outbound

This paper cites an unresolved cited work.

A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

Reference 1

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Codensity and the Giry monad

Reference 2

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This paper cites An algebraic theory of Markov processes.

A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics An algebraic theory of Markov processes

Reference 3

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics A generalized no-broadcasting theorem

Reference 4

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This paper cites On statistics independent of a complete suﬃcient statistic.

A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics On statistics independent of a complete suﬃcient statistic

Reference 5

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This paper cites Selected works of Debabrata Basu.

A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Selected works of Debabrata Basu

Reference 6

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Probability and measure

Reference 7

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This paper cites Kantorovich problems and conditional measures depending on a parameter.

A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Kantorovich problems and conditional measures depending on a parameter

Reference 8

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Carboni and R

Reference 9

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Matrices, relations, and group repre sentations

Reference 10

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

Reference 13

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Probabilistic theories with purification

Reference 14

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Disintegration and Bayesian Inversion via String Diagrams

Reference 15

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics An Introduction to Effectus Theory

Reference 16

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Probability theory

Reference 17

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Terminality implies non-signalling

Reference 18

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Causal categories: relativistically interacting processes

Reference 19

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Classical and qua ntum structuralism

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Picturing classical and quantum Bayesian inference

Reference 21

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Purity through Factorisation

Reference 22

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Borel Kernels and their Approximation, Categorically

Reference 23

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Philip Dawid

Reference 24

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Philip Dawid

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Philip Dawid

Reference 26

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Philip Dawid and Milan Studen´ y

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Causality in Bayesian Belief Networks

Reference 29

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Compositories and glea ves

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Causal Theories: A Categorical Perspective on Bayesian Networks

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics The Algebra of Open and Interconnected Systems

Reference 35

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Supplying bells and whistles in symmetric monoidal categories

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Hypergraph Categories

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics What is Stochastic Independence?

Reference 38

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Beyond Bell's Theorem II: Scenarios with arbitrary causal structure

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Bimonoidal Structure of Probability Monads

Reference 42

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Probability, valuations, hyperspace: Three monads on Top and the support as a morphism

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Infinite products and zero-one laws in categorical probability

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Varieties of eﬀects

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Categorical Duality in Probability and Quantum Foundation s

Reference 46

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics From Kleisli Categories to Commutative C*-algebras: Probabilistic Gelfand Duality

Reference 47

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Categorial Independence and L\'evy Processes

Reference 48

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics A categorical approach to probability t heory

Reference 49

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Golubtsov

Reference 51

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Golubtsov

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Golubtsov

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Golubtsov

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Golubtsov

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Golubtsov

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Method of Additional Structures on the Objects of a Monoidal Kleisli Category as a Background for Information Transformers Theory

Reference 57

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Algebras of the extended probabilistic powerdomain monad

Reference 58

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Sur quelques points d’alg` eb re homologique

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Tenseurs et machines

Reference 60

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Halmos and L

Reference 61

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Spaces of valuations

Reference 62

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics A Convenient Category for Higher-Order Probability Theory

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Categories for Quantum Theory: An Introduction

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Symmetric monoidal sketc hes

Reference 65

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Semantics of weakening and contraction

Reference 66

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Structured probabilistic reasoning, 201 9

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:53.907022Z

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Categoric al semantics for arrows

Reference 68

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics A predicate/state transf ormer semantics for Bayesian learning

Reference 69

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Probabilistic morphisms and Bayesian nonparametrics

Reference 70

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics The geometry of tensor cal culus

Reference 71

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Ergodic theory of random transformations , volume 10 of Progress in Probability and Statistics

Reference 72

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Finite matrices are complete for (dagger-)hypergraph categories

Reference 73

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Equivalence of relativistic causal structure and process terminality

Reference 74

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

Reference 75

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Probability theory

Reference 76

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Lauritzen

Reference 77

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Lauritzen

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T12:37:53.822240Z

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Lauritzen and Frank Jensen

Reference 79

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics The category of probabilistic mappings, 1962

Reference 80

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

Reference 81

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Reference 82

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Monoidal categories with projections, 2 016

Reference 83

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Monoidal categories with projections, 2 016

Reference 84

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Modell ing environments in call-by-value programming languages

Reference 85

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Functional distribution monads in functional-analytic contexts

Reference 86

Resolution: verified exact; local_arxiv, observed 2026-08-14T12:37:53.127948Z

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This paper cites Categories for the working mathematician , volume 5 of Graduate Texts in Mathe- matics.

A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Categories for the working mathematician , volume 5 of Graduate Texts in Mathe- matics

Reference 87

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Sheaves in geometry and logic

Reference 88

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

Reference 89

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Soﬁa Massa and Steﬀen L

Reference 90

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics What is a statistical model? Ann

Reference 91

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

Reference 92

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Statistical isom orphism

Reference 93

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Bases math´ ematiques du calcul des probabilit´ es

Reference 94

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Labelled Markov processes

Reference 95

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Causality

Reference 96

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Graphoids: a graph-based lo gic for reasoning about relevance relations,

Reference 97

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Categorical Probability and Stochastic Dominance in Metri c Spaces

Reference 98

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Caract´ erisation des cat´ egories ab´ eliennes avec g´ en´ erateurs et limites inductives exactes

Reference 99

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A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics Unresolved cited work

Reference 100

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Pith citing papers

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Supplying bells and whistles in symmetric monoidal categories cites this paper.

Supplying bells and whistles in symmetric monoidal categories A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics

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Categorical and geometric methods in statistical, manifold, and machine learning A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics

Reference 26

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Finite Observations, Infinite Behaviour: bicategorical semantics for stateful monoidal processes A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics

Reference 28

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Convex Biproducts, Stochastic Matrices and Tape Diagrams cites this paper.

Convex Biproducts, Stochastic Matrices and Tape Diagrams A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics

Reference 221

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Local Uniqueness of the Born Rule on Categories with Complex-Weighted Morphisms cites this paper.

Local Uniqueness of the Born Rule on Categories with Complex-Weighted Morphisms A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics

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