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Archive of posts filed under the Statistical computing category.

What does CNN have in common with Carmen Reinhart, Kenneth Rogoff, and Richard Tol: They all made foolish, embarrassing errors that would never have happened had they been using R Markdown

Rachel Cunliffe shares this delight: Had the CNN team used an integrated statistical analysis and display system such as R Markdown, nobody would’ve needed to type in the numbers by hand, and the above embarrassment never would’ve occurred. And CNN should be embarrassed about this: it’s much worse than a simple typo, as it indicates […]

Bayesian Cognitive Modeling  Examples Ported to Stan

There’s a new intro to Bayes in town. Michael Lee and Eric-Jan Wagenmaker. 2014. Bayesian Cognitive Modeling: A Practical Course. Cambridge Uni. Press. This book’s a wonderful introduction to applied Bayesian modeling. But don’t take my word for it — you can download and read the first two parts of the book (hundreds of pages […]

My talk with David Schiminovich this Wed noon: “The Birth of the Universe and the Fate of the Earth: One Trillion UV Photons Meet Stan”

This talk will have two parts. (1) Astronomy professor David Schiminovich will discuss the ways in which recent large-scale sky surveys that include billions of data points can address questions such as, What will happen to the Earth and other planets when the Sun becomes a white dwarf? (2) Statistics professor Andrew Gelman will discuss […]

Dave Blei course on Foundations of Graphical Models

Dave Blei writes: This course is cross listed in Computer Science and Statistics at Columbia University. It is a PhD level course about applied probabilistic modeling. Loosely, it will be similar to this course. Students should have some background in probability, college-level mathematics (calculus, linear algebra), and be comfortable with computer programming. The course is […]

How Many Mic’s Do We Rip

Yakir Reshef writes: Our technical comment on Kinney and Atwal’s paper on MIC and equitability has come out in PNAS along with their response. Similarly to Ben Murrell, who also wrote you a note when he published a technical comment on the same work, we feel that they “somewhat missed the point.” Specifically: one statistic […]

“A hard case for Mister P”

Kevin Van Horn sent me an email with the above title (ok, he wrote MRP, but it’s the same idea) and the following content: I’m working on a problem that at first seemed like a clear case where multilevel modeling would be useful. As I’ve dug into it I’ve found that it doesn’t quite fit […]

Cool new position available: Director of the Pew Research Center Labs

Peter Henne writes: I wanted to let you know about a new opportunity at Pew Research Center for a data scientist that might be relevant to some of your colleagues. I [Henne] am a researcher with the Pew Research Center, where I manage an international index on religious issues. I am also working with others […]

Stanny Stanny Stannitude

On the stan-users list, Richard McElreath reports: With 2.4 out, I ran a quick test of how much speedup I could get by changing my old non-vectorized multi_normal sampling to the new vectorized form. I get a 40% time savings, without even trying hard. This is much better than I expected. Timings with vectorized multi_normal: […]

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 2.4, New and Improved

We’re happy to announce that all three interfaces (CmdStan, PyStan, and RStan) are up and ready to go for Stan 2.4. As usual, you can find full instructions for installation on the Stan Home Page. Here are the release notes with a list of what’s new and improved: New Features ———— * L-BFGS optimization (now […]