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Archive of posts filed under the Stan category.

Stan 2.5, now with MATLAB, Julia, and ODEs

As usual, you can find everything on the Stan Home Page. Drop us a line on the stan-users group if you have problems with installs or questions about Stan or coding particular models. New Interfaces We’d like to welcome two new interfaces: MatlabStan by Brian Lau, and  Stan.jl (for Julia) by Rob Goedman. The new […]

“We have used Stan to study dead dolphins”

In response to our call for references to successful research using Stan, Matthieu Authier points us to this: @article{ year={2014}, journal={Biodiversity and Conservation}, volume={23}, number={10}, doi={10.1007/s10531-014-0741-3}, title={How much are stranding records affected by variation in reporting rates? A case study of small delphinids in the Bay of Biscay}, url={}, keywords={Monitoring; Marine mammal; Strandings}, author={Authier, Matthieu […]

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 […]

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: […]

Stan NYC Meetup – Thurs, July 31

The next Stan NYC meetup is happening on Thursday, July 31, at 7 pm. If you’re interested, registration is required and closes on Wednesday night:   The third session will focus on using the Stan language. If you’re bringing a laptop, please come with RStan, PyStan, or CmdStan already installed.   We’re going to […]

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 […]

Stan found using directed search

X and I did some “Sampling Through Adaptive Neighborhoods” ourselves the other day and checked out the nearby grave of Stanislaw Ulam, who is buried with his wife, Fran├žoise Aron, and others of her family. The above image of Stanislaw and Fran├žoise Ulam comes from this charming mini-biography from Roland Brasseur, which I found here. […]

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 […]