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Archive of posts filed under the Bayesian Statistics category.

Bayesian inference: The advantages and the risks

This came up in an email exchange regarding a plan to come up with and evaluate Bayesian prediction algorithms for a medical application: I would not refer to the existing prediction algorithm as frequentist. Frequentist refers to the evaluation of statistical procedures but it doesn’t really say where the estimate or prediction comes from. Rather, […]

New Alan Turing preprint on Arxiv!

Dan Kahan writes: I know you are on 30-day delay, but since the blog version of you will be talking about Bayesian inference in couple of hours, you might like to look at paper by Turing, who is on 70-yr delay thanks to British declassification system, who addresses the utility of using likelihood ratios for […]

“Do we have any recommendations for priors for student_t’s degrees of freedom parameter?”

In response to the above question, Aki writes: I recommend as an easy default option real nu; nu ~ gamma(2,0.1); This was proposed and anlysed by Juárez and Steel (2010) (Model-based clustering of non-Gaussian panel data based on skew-t distributions. Journal of Business & Economic Statistics 28, 52–66.). Juárez and Steel compere this to Jeffreys […]

My talk at MIT this Thursday

When I was a student at MIT, there was no statistics department. I took a statistics course from Stephan Morgenthaler and liked it. (I’d already taken probability and stochastic processes back at the University of Maryland; my instructor in the latter class was Prof. Grace Yang, who was super-nice. I couldn’t follow half of what […]

There’s something about humans

An interesting point came up recently. In the abstract to my psychology talk, I’d raised the question: If we can’t trust p-values, does experimental science involving human variation just have to start over? In the comments, Rahul wrote: Isn’t the qualifier about human variation redundant? If we cannot trust p-values we cannot trust p-values. My […]

What I got wrong (and right) about econometrics and unbiasedness

Yesterday I spoke at the Princeton economics department. The title of my talk was: “Unbiasedness”: You keep using that word. I do not think it means what you think it means. The talk went all right—people seemed ok with what I was saying—but I didn’t see a lot of audience involvement. It was a bit […]

“The general problem I have with noninformatively-derived Bayesian probabilities is that they tend to be too strong.”

We interrupt our usual programming of mockery of buffoons to discuss a bit of statistical theory . . . Continuing from yesterday‘s quotation of my 2012 article in Epidemiology: Like many Bayesians, I have often represented classical confidence intervals as posterior probability intervals and interpreted one-sided p-values as the posterior probability of a positive effect. […]

Good, mediocre, and bad p-values

From my 2012 article in Epidemiology: In theory the p-value is a continuous measure of evidence, but in practice it is typically trichotomized approximately into strong evidence, weak evidence, and no evidence (these can also be labeled highly significant, marginally significant, and not statistically significant at conventional levels), with cutoffs roughly at p=0.01 and 0.10. […]

Carl Morris: Man Out of Time [reflections on empirical Bayes]

I wrote the following for the occasion of his recent retirement party but I thought these thoughts might of general interest: When Carl Morris came to our department in 1989, I and my fellow students were so excited. We all took his class. The funny thing is, though, the late 1980s might well have been […]

Instead of worrying about multiple hypothesis correction, just fit a hierarchical model.

Pejman Mohammadi writes: I’m concerned with a problem in multiple hypothesis correction and, despite having read your article [with Jennifer and Masanao] on not being concerned about it, I was hoping I could seek your advice. Specifically, I’m interested in multiple hypothesis testing problem in cases when the test is done with a discrete finite […]