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

  title={A synthetic approach to Markov kernels, conditional independence, and theorems on sufficient statistics},
  author={T. Fritz},
  • 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
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