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Archive of posts filed under the Statistical computing category.

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 […]

NYC workshop 22 Aug on open source machine learning systems

The workshop is organized by John Langford (Microsoft Research NYC), along with Alekh Agarwal and Alina Beygelzimer, and it features Liblinear, Vowpal Wabbit, Torch, Theano, and . . . you guessed it . . . Stan! Here’s the current program: 8:55am: Introduction 9:00am: Liblinear by CJ Lin. 9:30am: Vowpal Wabbit and Learning to Search (John […]

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. […]

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 […]