• DOI:10.1016/j.aim.2020.107239
Corpus ID: 201103837

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

@article{Fritz2019ASA,
  title={A synthetic approach to Markov kernels, conditional independence, and theorems on sufficient statistics},
  author={T. Fritz},
  journal={ArXiv},
  year={2019},
  volume={abs/1908.07021}
}

T. Fritz

Published 2019

Mathematics, Computer Science

ArXiv

Abstract We develop Markov categories as a framework for synthetic probability and statistics, following work of Golubtsov as well as Cho and Jacobs. This means that we treat the following concepts in purely abstract categorical terms: conditioning and disintegration; various versions of conditional independence and its standard properties; conditional products; almost surely; sufficient statistics; versions of theorems on sufficient statistics due to Fisher–Neyman, Basu, and Bahadur. Besides… CONTINUE READING

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed