As a coincidence, I noticed that Don Fraser’s recent discussion paper `Is Bayes posterior just quick and dirty confidence?’ will be discussed this Friday (18:00 UTC) on the Cross Validated Journal Club. I do not know whether or not to interpret the information “The author confirmed his presence at the event” as meaning Don Fraser will be on line to discuss his paper with X’ed members Feel free to join anyway if you have 20 reputation points or plan to get those by Friday! (I will be in the train coming back from Oxford. Oxford, England, not Mississippi!)
Archive for Don Fraser
Our paper with Jim Hobert and Vivek Roy, Improving the Convergence Properties of the Data Augmentation Algorithm with an Application to Bayesian Mixture Modeling, has now appeared in Statistical Science and is available on Project Euclid. (For IMS members, at least.) Personally, this is an important paper, not only for providing an exact convergence evaluation for mixtures, not only for sharing exciting research days with my friends Jim and Vivek, but also for finalising a line of research somehow started in 1993 when Richard Tweedie visited me in Paris and when I visited him in Fort Collins… Coincidentally, my discussion of Don Fraser’s provocative Is Bayes Posterior just Quick and Dirty Confidence? also appeared in this issue of Statistical Science.
“How can a discipline, central to science and to critical thinking, have two methodologies, two logics, two approaches that frequently give substantially different answers to the same problems. Any astute person from outside would say “Why don’t they put their house in order?”” Don Fraser
Following the discussions of his Statistical Science paper Is Bayes posterior just quick and dirty confidence?, by Kesar Singh and Minge Xie, Larry Wasserman (who coined the neologism Frasian for the occasion), Tong Zhang, and myself, Don Fraser has written his rejoinder to the discussion (although in Biometrika style it is for Statistical Science!). His conclusion that “no one argued that the use of the conditional probability lemma with an imaginary input had powers beyond confidence, supernatural powers” is difficult to escape, as I would not dream of promoting a super-Bayes jumping to the rescue of bystanders misled by evil frequentists!!! More seriously, this rejoinder makes me reflect on lectures from the past years, from those on the diverse notions of probability (Jeffreys, Keynes, von Mises, and Burdzy) to those on scientific discovery (mostly Seber‘s, and the promising Error and Inference by Mayo and Spanos I just received).