  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 the conceptual clarity offered by our categorical setup, its main
advantage is that it provides a uniform treatment of various types of
probability theory, including discrete probability theory, measure-theoretic
probability with general measurable spaces, Gaussian probability, stochastic
processes of either of these kinds, and many others.
