Probability matching priors, ensuring frequentist validity of
posterior credible sets up to the desired order of asymptotics, are
of substantial current interest. They can form the basis of an
objective Bayesian analysis. In addition, they provide a route for
obtaining accurate frequentist confidence sets, which are
meaningful also to a Bayesian. This monograph presents, for the
first time in book form, an up-to-date and comprehensive account of
probability matching priors addressing the problems of both
estimation and prediction. Apart from being useful to researchers,
it can be the core of a one-semester graduate course in Bayesian
asymptotics.
Gauri Sankar Datta is a professor of statistics at the
University of Georgia. He has published extensively in the fields
of Bayesian analysis, likelihood inference, survey sampling, and
multivariate analysis.
Rahul Mukerjee is a professor of statistics at the Indian
Institute of Management Calcutta. He co-authored three other
research monographs, including "A Calculus for Factorial
Arrangements" in this series. A fellow of the Institute of
Mathematical Statistics, Dr. Mukerjee is on the editorial boards of
several international journals.
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