Bradley Efron - Frequentist accuracy of Bayesian estimates

Presented by Bradley Efron, Max H Stein Professor of Humanities and Sciences, Professor of Statistics at Stanford University.
Discussant: Andrew Gelman of Columbia University.
Chair: Peter Diggle.
Bradley's paper 'Frequentist accuracy of Bayesian estimates' was recently published in the Royal Statistical Society's Series B Journal (Volume 77 (2015), part 3). The abstract is as follows:
In the absence of relevant prior experience, popular Bayesian estimation techniques usually begin with some form of 'uninformative' prior distribution intended to have minimal inferential influence. Bayes' rule will still produce nice-looking estimates and credible intervals, but these lack the logical force attached to experience-based priors and require further justification. This paper concerns the frequentist assessment of Bayes estimates. A simple formula is shown to give the frequentist standard deviation of a Bayesian point estimate. The same simulations required for the point estimate also produce the standard deviation. Exponential family models make the calculations particularly simple, and bring in a connection to the parametric bootstrap.
Bradley Efron is Max H Stein professor of humanities and sciences, professor of statistics at Stanford University, and professor of biostatistics with the Department of Health Research and Policy in the School of Medicine. He is a former president of both the American Statistical Association and the Institute of Mathematical Statistics. A recipient of the Ford Prize of the Mathematical Association of America and of both the Wilks Medal and the Noether Prize from the American Statistical Association (ASA). In 2003 Bradley was given the inaugural Rao Prize for outstanding research in statistics by Pennsylvania State University in 2005 he received the National Medal of Science. In 2014, Bradley was awarded the Guy Medal in Gold by the Royal Statistical Society for his 'seminal contributions to many areas of statistics'.