Bayes is typically wrong…

In Harvard, this morning, Don Fraser gave a talk at the Bayesian, Fiducial, and Frequentist conference where he repeated [as shown by the above quote] the rather harsh criticisms on Bayesian inference he published last year in Statistical Science. And which I discussed a few days ago. The “wrongness” of Bayes starts with the completely arbitrary choice of the prior, which Don sees as unacceptable, and then increases because the credible regions are not confident regions, outside natural parameters from exponential families (Welch and Peers, 1963). And one-dimensional parameters using the profile likelihood (although I cannot find a proper definition of what the profile likelihood is in the paper, apparently a plug-in version that is not a genuine likelihood, hence somewhat falling under the same this-is-not-a-true-probability cleaver as the disputed Bayesian approach).

“I expect we’re all missing something, but I do not know what it is.” D.R. Cox, Statistical Science, 1994

And then Nancy Reid delivered a plenary lecture “Are we converging?” on the afternoon that compared most principles (including objective if not subjective Bayes) against different criteria, like consistency, nuisance elimination, calibration, meaning of probability, and so on.  In an highly analytic if pessimistic panorama. (The talk should be available on line at some point soon.)

4 Responses to “Bayes is typically wrong…”

  1. For something that is “typically wrong”, it’s amazing how often it’s right!

    His is an old, hackneyed, “frequentist” argument that doesn’t hold up any longer in today’s digital powered world.

  2. That “1996” is a typo in Reid’s presentation; the actual date is 1994. I don’t know if you did this on purpose, but hilariously the quote itself is missing something: the word “know”.

    • Indeed the typo of forgetting know [now corrected] is hilarious. This is what happens when blogging during an interesting talk, while jet-lagged and on (tea) caffeine for too long… And thanks for providing a set of slides that comes close to the one Nancy presented yesterday.

  3. Sapiens are always wrong

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