On the Markov chain central limit theorem.
Evaluating default priors with a generalization of Eaton’s Markov chain
We consider evaluating improper priors in a formal Bayes setting according to the consequences of their use. Let be a class of functions on the parameter space and consider estimating elements of under quadratic loss. If the formal Bayes estimator of every function in is admissible, then the prior is strongly admissible with respect to . Eaton’s method for establishing strong admissibility is based on studying the stability properties of a particular Markov chain associated with the inferential...
On perturbations of strongly admissible prior distributions
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