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Archive of posts filed under the Stan category.

Practical Bayesian model evaluation in Stan and rstanarm using leave-one-out cross-validation

Our (Aki, Andrew and Jonah) paper Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC was recently published in Statistics and Computing. In the paper we show why it’s better to use LOO instead of WAIC for model evaluation how to compute LOO quickly and reliably using the full posterior sample how Pareto smoothing importance […]

Mathematica, now with Stan

Vincent Picaud developed a Mathematica interface to Stan: MathematicaStan You can find everything you need to get started by following the link above. If you have questions, comments, or suggestions, please let us know through the Stan user’s group or the GitHub issue tracker. MathematicaStan interfaces to Stan through a CmdStan process. Stan programs are […]

Webinar: Introduction to Bayesian Data Analysis and Stan

This post is by Eric. We are starting a series of free webinars about Stan, Bayesian inference, decision theory, and model building. The first webinar will be held on Tuesday, October 25 at 11:00 AM EDT. You can register here. Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this […]

Stan case studies!

In the spirit of reproducible research, we (that is, Bob*) set up this beautiful page of Stan case studies. Check it out. * Bob here. Michael set the site up, I set this page up, and lots of people have contributed case studies and we’re always looking for more to publish.

StanCon: now accepting registrations and submissions

As we announced here a few weeks ago, the first Stan conference will be Saturday, January 21, 2017 at Columbia University in New York. We are now accepting both conference registrations and submissions. Full details are available at StanCon page on the Stan website. If you have any questions please let us know and we […]

A book on RStan in Japanese: Bayesian Statistical Modeling Using Stan and R (Wonderful R, Volume 2)

Wonderful, indeed, to have an RStan book in Japanese: Kentarou Matsuura. 2016. Bayesian Statistical Modeling Using Stan and R. Wonderful R Series, Volume 2. Kyoritsu Shuppan Co., Ltd. Google translate makes the following of the description posted on Amazon Japan (linked from the title above): In recent years, understanding of the phenomenon by fitting a […]

“Crimes Against Data”: My talk at Ohio State University this Thurs; “Solving Statistics Problems Using Stan”: My talk at the University of Michigan this Fri

Crimes Against Data Statistics has been described as the science of uncertainty. But, paradoxically, statistical methods are often used to create a sense of certainty where none should exist. The social sciences have been rocked in recent years by highly publicized claims, published in top journals, that were reported as “statistically significant” but are implausible […]

StanCon is coming! Sat, 1/21/2017

Save the date! The first Stan conference is going to be in NYC in January. Registration will open at the end of September.   When: Saturday, January 21, 2017 9 am – 5 pm   Where: Davis Auditorium, Columbia University 530 West 120th Street 4th floor (campus level), room 412 New York, NY 10027   […]

Solving Statistics Problems Using Stan (my talk at the NYC chapter of the American Statistical Association)

Here’s the announcement: Solving Statistics Problems Using Stan Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this talk, we demonstrate the use of Stan for some small fun problems and then discuss some open problems in Stan and in Bayesian computation and Bayesian inference more generally. It’s next Tues, […]

Stan wins again!

See here.

Stan users group hits 2000 registrations

Of course, there are bound to be duplicate emails, dead emails, and people who picked up Stan, joined the list, and never came back. But still, that’s a lot of people who’ve expressed interest! It’s been an amazing ride that’s only going to get better as we learn more and continue to improve Stan’s speed […]

Fast CAR: Two weird tricks for fast conditional autoregressive models in Stan

Max Joseph writes: Conditional autoregressive (CAR) models are popular as prior distributions for spatial random effects with areal spatial data. Historically, MCMC algorithms for CAR models have benefitted from efficient Gibbs sampling via full conditional distributions for the spatial random effects. But, these conditional specifications do not work in Stan, where the joint density needs […]

Who owns your code and text and who can use it legally? Copyright and licensing basics for open-source

I am not a lawyer (“IANAL” in web-speak); but even if I were, you should take this with a grain of salt (same way you take everything you hear from anyone). If you want the straight dope for U.S. law, see the U.S. government Copyright FAQ; it’s surprisingly clear for government legalese. What is copyrighted? […]

Free workshop on Stan for pharmacometrics (Paris, 22 September 2016); preceded by (non-free) three day course on Stan for pharmacometrics

So much for one post a day… Workshop: Stan for Pharmacometrics Day If you are interested in a free day of Stan for pharmacometrics in Paris on 22 September 2016, see the registration page: Stan for Pharmacometrics Day (free workshop) Julie Bertrand (statistical pharmacologist from Paris-Diderot and UCL) has finalized the program: When Who What […]

His varying slopes don’t seem to follow a normal distribution

Bruce Doré writes: I have a question about multilevel modeling I’m hoping you can help with. What should one do when random effects coefficients are clearly not normally distributed (i.e., coef(lmer(y~x+(x|id))) )? Is this a sign that the model should be changed? Or can you stick with this model and infer that the assumption of […]

Postdoc in Finland with Aki

I’m looking for a postdoc to work with me at Aalto University, Finland. The person hired will participate in research on Gaussian processes, functional constraints, big data, approximative Bayesian inference, model selection and assessment, deep learning, and survival analysis models (e.g. cardiovascular diseases and cancer). Methods will be implemented mostly in GPy and Stan. The […]

Stan Course up North (Anchorage, Alaska) 23–24 Aug 2016

Daniel Lee’s heading up to Anchorage, Alaska to teach a two-day Stan course at the Alaska chapter of the American Statistical Association (ASA) meeting in Anchorage. Here’s the rundown: Information and Free Registration I hear Alaska’s beautiful in the summer—16 hour days in August and high temps of 17 degrees celsius. Plus Stan! More Upcoming […]

A little story of the Folk Theorem of Statistical Computing

I know I promised I wouldn’t blog, but this one is so clean and simple. And I already wrote it for the stan-users list anyway so it’s almost no effort to post it here too: A colleague and I were working on a data analysis problem, had a very simple overdispersed Poisson regression with a […]

Smooth poll aggregation using state-space modeling in Stan, from Jim Savage

Jim Savage writes: I just saw your post on poll bounces; have been thinking the same myself. Why are the poll aggregators so jumpy about new polls? Annoyed, I put together a poll aggregator that took a state-space approach to the unobserved preferences; nothing more than the 8 schools (14 polls?) example with a time-varying […]

Stan 2.11 Good, Stan 2.10 Bad

Stan 2.11 is available for all interfaces We are happy to announce that all of the interfaces have been updated to Stan 2.11. There was a subtle bug introduced in 2.10 where a probabilistic acceptance condition was being checked twice. Sorry about that and thanks for your patience. We’ve added some additional tests to catch […]