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: […]

**Statistical computing**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 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 […]

## Comment of the week

This one, from DominikM: Really great, the simple random intercept – random slope mixed model I did yesterday now runs at least an order of magnitude faster after installing RStan 2.3 this morning. You are doing an awesome job, thanks a lot!

## (Py, R, Cmd) Stan 2.3 Released

We’re happy to announce RStan, PyStan and CmdStan 2.3. Instructions on how to install at: http://mc-stan.org/ As always, let us know if you’re having problems or have comments or suggestions. We’re hoping to roll out the next release a bit quicker this time, because we have lots of good new features that are almost ready […]

## Judicious Bayesian Analysis to Get Frequentist Confidence Intervals

Christian Bartels has a new paper, “Efficient generic integration algorithm to determine confidence intervals and p-values for hypothesis testing,” of which he writes: The paper proposes to do an analysis of observed data which may be characterized as doing a judicious Bayesian analysis of the data resulting in the determination of exact frequentist p-values and […]