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Archive of posts filed under the Bayesian Statistics category.

Bayesian survival analysis with horseshoe priors—in Stan!

Tomi Peltola, Aki Havulinna, Veikko Salomaa, and Aki Vehtari write: This paper describes an application of Bayesian linear survival regression . . . We compare the Gaussian, Laplace and horseshoe shrinkage priors, and find that the last has the best predictive performance and shrinks strong predictors less than the others. . . . And here’s […]

Stan Down Under

I (Bob, not Andrew) am in Australia until April 30. I’ll be giving some Stan-related and some data annotation talks, several of which have yet to be concretely scheduled. I’ll keep this page updated with what I’ll be up to. All of the talks other than summer school will be open to the public (the […]

Why I keep talking about “generalizing from sample to population”

Someone publishes some claim, some statistical comparison with “p less than .05″ attached to it. My response is: OK, you see this pattern in the sample. Do you think it holds in the population? Why do I ask this? Why don’t I ask the more standard question: Do you really think this result is statistically […]

Total survey error

Erez Shalom writes: It’s election time in Israel and every week several surveys come out trying to predict the ‘mandates’ that each party will get (out of a total of 120). These surveys are historically flakey, and no one takes the ‘sampling error’ they come with seriously, but no one has a good idea of […]

This has nothing to do with the Super Bowl

Joshua Vogelstein writes: The Open Connectome Project at Johns Hopkins University invites outstanding candidates to apply for a postdoctoral or assistant research scientist position in the area of statistical machine learning for big brain imaging data. Our workflow is tightly vertically integrated, ranging from raw data to theory to answering neuroscience questions and back again. […]

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 […]