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Archive of posts filed under the Bayesian Statistics category.

Where does Mister P draw the line?

Bill Harris writes: Mr. P is pretty impressive, but I’m not sure how far to push him in particular and MLM [multilevel modeling] in general. Mr. P and MLM certainly seem to do well with problems such as eight schools, radon, or the Xbox survey. In those cases, one can make reasonable claims that the […]

Interpreting posterior probabilities in the context of weakly informative priors

Nathan Lemoine writes: I’m an ecologist, and I typically work with small sample sizes from field experiments, which have highly variable data. I analyze almost all of my data now using hierarchical models, but I’ve been wondering about my interpretation of the posterior distributions. I’ve read your blog, several of your papers (Gelman and Weakliem, […]

How tall is Kit Harrington? Stan wants to know.

We interrupt our regularly scheduled programming for a special announcement. Madeleine Davies writes: “Here are some photos of Kit Harington. Do you know how tall he is?” I’m reminded, of course, of our discussion of the height of professional tall person Jon Lee Anderson: Full Bayes, please. I can’t promise publication on Gawker, but I’ll […]

“Best Linear Unbiased Prediction” is exactly like the Holy Roman Empire

Dan Gianola pointed me to this article, “One Hundred Years of Statistical Developments in Animal Breeding,” coauthored with Guilherme Rosa, which begins: Statistical methodology has played a key role in scientific animal breeding. Approximately one hundred years of statistical developments in animal breeding are reviewed. Some of the scientific foundations of the field are discussed, […]

The posterior distribution of the likelihood ratio as a summary of evidence

Gabriel Marinello writes: I am a PhD student in Astrophysics and am writing this email to you because an enquiry about point null hypothesis testing (H0: Theta = Theta0 and H1: Theta != Theta0) in a bayesian context and I think that your pragmatic stance would be helpful. In Astrophysics is not rare to find […]

A quick one

Fabio Rojas asks: Should I do Bonferroni adjustments? Pros? Cons? Do you have a blog post on this? Most social scientists don’t seem to be aware of this issue. My short answer is that if you’re fitting mutlilevel models, I don’t think you need multiple comparisons adjustments; see here.

Cross-validation != magic

In a post entitled “A subtle way to over-fit,” John Cook writes: If you train a model on a set of data, it should fit that data well. The hope, however, is that it will fit a new set of data well. So in machine learning and statistics, people split their data into two parts. […]

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 […]