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Archive of posts filed under the Bayesian Statistics category.

SciLua 2 includes NUTS

The most recent release of SciLua includes an implementation of Matt’s sampler, NUTS (link is to the final JMLR paper, which is a revision of the earlier arXiv version). According to the author of SciLua, Stefano Peluchetti: Should be quite similar to your [Stan's] implementation with some differences in the adaptation strategy. If you have […]

Stan World Cup update

The other day I fit a simple model to estimate team abilities from World Cup outcomes. I fit the model to the signed square roots of the score differentials, using the square root on the theory that when the game is less close, it becomes more variable. 0. Background As you might recall, the estimated […]

Stan goes to the World Cup

I thought it would be fun to fit a simple model in Stan to estimate the abilities of the teams in the World Cup, then I could post everything here on the blog, the whole story of the analysis from beginning to end, showing the results of spending a couple hours on a data analysis. […]

Chicago alert: Mister P and Stan to be interviewed on WBEZ today (Fri) 3:15pm

Niala Boodho on the Afternoon Shift will be interviewing Yair and me about our age-period-cohort extravaganza which became widely-known after being featured in this cool interactive graph by Amanda Cox in the New York Times. And here’s the interview. The actual paper is called The Great Society, Reagan’s revolution, and generations of presidential voting and […]

“P.S. Is anyone working on hierarchical survival models?”

Someone who wishes to remain anonymous writes: I’m working on building a predictive model (not causal) of the onset of diabetes mellitus using electronic medical records from a semi-panel of HMO patients. The dependent variable is blood glucose level. The unit of analysis is the patient visit to a network doctor or hospitalization in a […]

“Bayes Data Analysis – Author Needed”

The following item came in over the Bayes email list: Hi, My name is Jo Fitzpatrick and I work as an Acquisition Editor for Packt Publishing ( www.packtpub.com ). We recently commissioned a book on Bayesian Data Analysis and I’m currently searching for an author to write this book. You need to have good working […]

Dimensionless analysis as applied to swimming!

We have no fireworks-related posts for July 4th but at least we have an item that’s appropriate for the summer weather. It comes from Daniel Lakeland, who writes: Recently in one of your blog posts (“priors I don’t believe”) there was a discussion in which I was advocating the use of dimensional analysis and dimensionless […]

“The great advantage of the model-based over the ad hoc approach, it seems to me, is that at any given time we know what we are doing.”

The quote is from George Box, 1979. And this: Please can Data Analysts get themselves together again and become whole Statisticians before it is too late? Before they, their employers, and their clients forget the other equally important parts of the job statisticians should be doing, such as designing investigations and building models? I actually […]

“Being an informed Bayesian: Assessing prior informativeness and prior–likelihood conflict”

Xiao-Li Meng sends along this paper (coauthored with Matthew Reimherr and Dan Nicolae), which begins: Dramatically expanded routine adoption of the Bayesian approach has substantially increased the need to assess both the confirmatory and contradictory information in our prior distribution with regard to the information provided by our likelihood function. We propose a diagnostic approach […]

Useless Algebra, Inefficient Computation, and Opaque Model Specifications

I (Bob, not Andrew) doubt anyone sets out to do algebra for the fun of it, implement an inefficient algorithm, or write a paper where it’s not clear what the model is. But… Why not write it in BUGS or Stan? Over on the Stan users group, Robert Grant wrote Hello everybody, I’ve just been […]