  In this paper, we evaluate the challenges and best practices associated with
the Markov bases approach to sampling from conditional distributions. We
provide insights and clarifications after 25 years of the publication of the
fundamental theorem for Markov bases by Diaconis and Sturmfels. In addition to
a literature review we prove three new results on the complexity of Markov
bases in hierarchical models, relaxations of the fibers in log-linear models,
and limitations of partial sets of moves in providing an irreducible Markov
chain.
