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Archive of posts filed under the Bayesian Statistics category.

About a zillion people pointed me to yesterday’s xkcd cartoon

I have the same problem with Bayes factors, for example this: and this: (which I copied from Wikipedia, except that, unlike you-know-who, I didn’t change the n’s to d’s and remove the superscripting). Either way, I don’t buy the numbers, and I certainly don’t buy the words that go with them. I do admit, though, […]

“It is perhaps merely an accident of history that skeptics and subjectivists alike strain on the gnat of the prior distribution while swallowing the camel that is the likelihood”

I recently bumped into this 2013 paper by Christian Robert and myself, “‘Not Only Defended But Also Applied': The Perceived Absurdity of Bayesian Inference,” which begins: Younger readers of this journal may not be fully aware of the passionate battles over Bayesian inference among statisticians in the last half of the twentieth century. During this […]

“The Statistical Crisis in Science”: My talk this Thurs at the Harvard psychology department

Noon Thursday, January 29, 2015, in William James Hall 765 room 1: The Statistical Crisis in Science Andrew Gelman, Dept of Statistics and Dept of Political Science, Columbia University Top journals in psychology routinely publish ridiculous, scientifically implausible claims, justified based on “p < 0.05.” And this in turn calls into question all sorts of […]

“What then should we teach about hypothesis testing?”

Someone who wishes to remain anonymous writes in: Last week, I was looking forward to a blog post titled “Why continue to teach and use hypothesis testing?” I presume that this scheduled post merely became preempted by more timely posts. But I am still interested in reading the exchange that will follow. My feeling is […]

Cross-validation, LOO and WAIC for time series

This post is by Aki. Jonah asked in Stan users mailing list Suppose we have J groups and T time periods, so y[t,j] is the observed value of y at time t for group j. (We also have predictors x[t,j].) I’m wondering if WAIC is appropriate in this scenario assuming that our interest in predictive accuracy is for […]

Stan comes through . . . again!

Erikson Kaszubowski writes in: I missed your call for Stan research stories, but the recent post about stranded dolphins mentioned it again. When I read about the Crowdstorming project in your blog, I thought it would be a good project to apply my recent studies in Bayesian modeling. The project coordinators shared a big dataset […]

Planning my class for this semester: Thinking aloud about how to move toward active learning?

I’m teaching two classes this semester: – Design and Analysis of Sample Surveys (in the political science department, but the course has lots of statistics content); – Statistical Communication and Graphics (in the statistics department, but last time I taught it, many of the students were from other fields). I’ve taught both classes before. I […]

“Why continue to teach and use hypothesis testing?”

Greg Werbin points us to an online discussion of the following question: Why continue to teach and use hypothesis testing (with all its difficult concepts and which are among the most statistical sins) for problems where there is an interval estimator (confidence, bootstrap, credibility or whatever)? What is the best explanation (if any) to be […]

The Use of Sampling Weights in Bayesian Hierarchical Models for Small Area Estimation

All this discussion of plagiarism is leaving a bad taste in my mouth (or, I guess I should say, a bad feeling in my fingers, given that I’m expressing all this on the keyboard) so I wanted to close off the workweek with something more interesting. I happened to come across the above-titled paper by […]

Expectation propagation as a way of life

Aki Vehtari, Pasi Jylänki, Christian Robert, Nicolas Chopin, John Cunningham, and I write: We revisit expectation propagation (EP) as a prototype for scalable algorithms that partition big datasets into many parts and analyze each part in parallel to perform inference of shared parameters. The algorithm should be particularly efficient for hierarchical models, for which the […]