Posts Tagged With: subjective/objective Bayesian

U-Phil: Deconstructions [of J. Berger]: Irony & Bad Faith 3

Memory Lane: 2 years ago:
My efficient Errorstat Blogpeople1 have put forward the following 3 reader-contributed interpretive efforts2 as a result of the “deconstruction” exercise from December 11, (mine, from the earlier blog, is at the end) of what I consider:

“….an especially intriguing remark by Jim Berger that I think bears upon the current mindset (Jim is aware of my efforts):

Too often I see people pretending to be subjectivists, and then using “weakly informative” priors that the objective Bayesian community knows are terrible and will give ridiculous answers; subjectivism is then being used as a shield to hide ignorance. . . . In my own more provocative moments, I claim that the only true subjectivists are the objective Bayesians, because they refuse to use subjectivism as a shield against criticism of sloppy pseudo-Bayesian practice. (Berger 2006, 463)” (From blogpost, Dec. 11, 2011)
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Andrew Gelman:

The statistics literature is big enough that I assume there really is some bad stuff out there that Berger is reacting to, but I think that when he’s talking about weakly informative priors, Berger is not referring to the work in this area that I like, as I think of weakly informative priors as specifically being designed to give answers that are _not_ “ridiculous.”

Keeping things unridiculous is what regularization’s all about, and one challenge of regularization (as compared to pure subjective priors) is that the answer to the question, What is a good regularizing prior?, will depend on the likelihood.  There’s a lot of interesting theory and practice relating to weakly informative priors for regularization, a lot out there that goes beyond the idea of noninformativity.

To put it another way:  We all know that there’s no such thing as a purely noninformative prior:  any model conveys some information.  But, more and more, I’m coming across applied problems where I wouldn’t want to be noninformative even if I could, problems where some weak prior information regularizes my inferences and keeps them sane and under control. Continue reading

Categories: Gelman, Irony and Bad Faith, J. Berger, Statistics, U-Phil | Tags: , , , | 3 Comments

U-PHIL: A Further Comment on Gelman by Christian Hennig (UCL, Statistics)

Comment on Gelman’s “Induction and Deduction in Bayesian Data Analysis” (RMM)

Dr. Christian Hennig (Senior Lecturer, Department of Statistical Science, University College London)

I have read quite a bit of what Andrew Gelman has written in recent years, including some of his blog. One thing that I find particularly refreshing and important about his approach is that he contrasts the Bayesian and frequentist philosophical conceptions honestly with what happens in the practice of data analysis, which often cannot (or does better not to) proceed according to an inflexible dogmatic book of rules.

I also like the emphasis on the fact that all models are wrong. I personally believe that a good philosophy of statistics should consistently take into account that models are rather tools for thinking than able to “match” reality, and in the vast majority of cases we know clearly that they are wrong (all continuous models are wrong because all observed data are discrete, for a start).

There is, however, one issue on which I find his approach unsatisfactory (or at least not well enough explained), and on which both frequentism and subjective Bayesianism seem superior to me.

Continue reading

Categories: Philosophy of Statistics, Statistics, U-Phil | Tags: , , , | 5 Comments

Mayo, Senn, and Wasserman on Gelman’s RMM** Contribution

Picking up the pieces...

Continuing with our discussion of contributions to the special topic,  Statistical Science and Philosophy of Science in Rationality, Markets and Morals (RMM),* I am pleased to post some comments on Andrew **Gelman’s paper “Induction and Deduction in Bayesian Data Analysis”.  (More comments to follow—as always, feel free to comment.)

Note: March 9, 2012: Gelman has commented to some of our comments on his blog today: http://andrewgelman.com/2012/03/coming-to-agreement-on-philosophy-of-statistics/

D. Mayo

For now, I will limit my own comments to two: First, a fairly uncontroversial point, while Gelman writes that “Popper has argued (convincingly, in my opinion) that scientific inference is not inductive but deductive,” a main point of my series (Part 123) of “No-Pain” philosophy was that “deductive” falsification involves inductively inferring a “falsifying hypothesis”.

More importantly, and more challengingly, Gelman claims the view he recommends “corresponds closely to the error-statistics idea of Mayo (1996)”.  Now the idea that non-Bayesian ideas might afford a foundation for strands of Bayesianism is not as implausible as it may seem. On the face of it, any inference to a claim, whether to the adequacy of a model (for a given purpose), or even to a posterior probability, can be said to be warranted just to the extent that the claim has withstood a severe test (i.e, a test that would, at least with reasonable probability, have discerned a flaw with the claim, were it false).  The question is: How well do Gelman’s methods for inferring statistical models satisfy severity criteria?  (I’m not sufficiently familiar with his intended applications to say.)

Continue reading

Categories: Philosophy of Statistics, Statistics, U-Phil | Tags: , , , | 1 Comment

Contributed Deconstructions: Irony & Bad Faith 3

My efficient Errorstat Blogpeople1 have put forward the following 3 reader-contributed interpretive efforts2 as a result of the “deconstruction” exercise from December 11, (mine, from the earlier blog, is at the end) of what I consider:

“….an especially intriguing remark by Jim Berger that I think bears upon the current mindset (Jim is aware of my efforts):

Too often I see people pretending to be subjectivists, and then using “weakly informative” priors that the objective Bayesian community knows are terrible and will give ridiculous answers; subjectivism is then being used as a shield to hide ignorance. . . . In my own more provocative moments, I claim that the only true subjectivists are the objective Bayesians, because they refuse to use subjectivism as a shield against criticism of sloppy pseudo-Bayesian practice. (Berger 2006, 463)” (From blogpost, Dec. 11, 2011) Continue reading

Categories: Irony and Bad Faith, Statistics, U-Phil | Tags: , , , | 11 Comments

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