Skip to content
Archive of posts filed under the Stan category.

Stan intro in Amherst, Mass.

Krzysztof Sakrejda writes: I’m doing a brief intro to Stan Thursday 4:30pm in Amherst at the University of Massachusetts. As the meetup blurb indicates I’m not going to attempt a full tour but I will try to touch on all the pieces required to make it easier to build on models from the manual and […]

PMXStan: an R package to facilitate Bayesian PKPD modeling with Stan

From Yuan Xiong, David A James, Fei He, and Wenping Wang at Novartis. Full version of the poster here.

Comparing Waic (or loo, or any other predictive error measure)

Ed Green writes: I have fitted 5 models in Stan and computed WAIC and its standard error for each. The standard errors are all roughly the same (all between 209 and 213). If WAIC_1 is within one standard error (of WAIC_1) of WAIC_2, is it fair to say that WAIC is inconclusive? My reply: No, […]

Solution to Stan Puzzle 1: Inferring Ability from Streaks

If you missed it the first time around, here’s a link to: Stan Puzzle 1: Inferring Ability from Streaks First, a hat-tip to Mike, who posted the correct answer as a comment. So as not to spoil the surprise for everyone else, Michael Betancourt (different Mike), emailed me the answer right away (as he always […]

Hot hand explanation again

I guess people really do read the Wall Street Journal . . . Edward Adelman sent me the above clipping and calculation and writes: What am I missing? I do not see the 60%. And Richard Rasiej sends me a longer note making the same point: So here I am, teaching another statistics class, this […]

RStan 2.8.0 is on CRAN!

RStan 2.8.0 is available on CRAN! Installation directions can be found on RStan’s Wiki. And since I know a lot of people aren’t patient enough to read through installation instructions, the most important parts are: You (still) need a C++ toolchain. Mac: XCode. Make sure to open it once after download to accept the license. […]

Fitting models with discrete parameters in Stan

This book, “Bayesian Cognitive Modeling: A Practical Course,” by Michael Lee and E. J. Wagenmakers, has a bunch of examples of Stan models with discrete parameters—mixture models of various sorts—with Stan code written by Martin Smira! It’s a good complement to the Finite Mixtures chapter in the Stan manual.

Stan Puzzle #1: Inferring Ability from Streaks

Inspired by X’s blog’s Le Monde puzzle entries, I have a little Stan coding puzzle for everyone (though you can solve the probabilty part of the coding problem without actually knowing Stan). This almost (heavy emphasis on “almost” there) makes me wish I was writing exams. Puzzle #1: Inferring Ability from Streaks Suppose a player […]

PK/PD Talk with Stan — Thu 8 Oct, 10:30 AM at Columbia: Improved confidence intervals and p-values by sampling from the normalized likelihood

Sebastian Ueckert and France Mentré are swinging by to visit the Stan team at Columbia and Sebastian’s presenting the following talk, to which everyone is invited. Improved confidence intervals and p-values by sampling from the normalized likelihood Sebastian Ueckert (1,2), Marie-Karelle Riviere (1), France Mentré (1) (1) IAME, UMR 1137, INSERM and University Paris Diderot, […]

Stan users meetup in Cambridge, MA on 9/22

There’s a new Stan users meetup group in Boston / Camberville. The first meeting will be on Tuesday, 9/22, at 6 pm in Cambridge. If you’re a seasoned Stan user, just starting out with Stan, or hearing about Stan for the first time, feel free to join in. At least a couple of the Stan […]

Let’s apply for some of that sweet, sweet National Sanitation Foundation funding

Paul Alper pointed me to this news article about where the bacteria and fungi hang out on airplanes. This is a topic that doesn’t interest me at all, but then I noticed this, at the very end of the article: Note: A previous version of this article cited the National Science Foundation rather than the […]

More Stan on the blog

Whoa. Stan is 3 years old. We’ve come a long way since the start. I came into the project just as a working prototype was implemented by Matt and Bob with discussions with Andrew, Ben, Michael Malecki, Jiqiang, and others. (I had been working for Andrew prior to the official start of the project, but […]

A Psych Science reader-participation game: Name this blog post

In a discussion of yesterday’s post on studies that don’t replicate, Nick Brown did me the time-wasting disservice of pointing out a recent press release from Psychological Science which, as you might have heard, is “the highest ranked empirical journal in psychology.” The press release is called “Blue and Seeing Blue: Sadness May Impair Color […]

Stan attribution

I worry that I get too much credit for Stan. So let me clarify. I didn’t write Stan. Stan is written in C++, and I’ve never in my life written a line of C, or C+, or C++, or C+++, or C-, or any of these things. Here’s a quick description of what we’ve all […]

Stan’s 3rd birthday!

Stan v1.0.0 was released on August 30, 2012. We’ve come a long way since. If you’re around and want to celebrate with some Stan developers and users, feel free to join us: Monday, August 31. 6 – 9 pm Untamed Sandwiches 43 W 39th St New York, NY If you didn’t know, we also have […]

Daniel on Stan at the NYC Machine Learning Meetup

I (Daniel) will be giving a Stan overview talk on Thursday, August 20, 7 pm. Bob gave a talk there 3.5 years ago. My talk will be light and include where we’ve been and where we’re going.   P.S. If you make it, find me. I have Stan stickers to give out. P.P.S. Stan is […]

My 2 classes this fall

Stat 6103, Bayesian Data Analysis Modern Bayesian methods offer an amazing toolbox for solving science and engineering problems. We will go through the book Bayesian Data Analysis and do applied statistical modeling using Stan, using R (or Python or Julia if you prefer) to preprocess the data and postprocess the analysis. We will also discuss […]

ShinyStan v2.0.0

For those of you not familiar with ShinyStan, it is a graphical user interface for exploring Stan models (and more generally MCMC output from any software). For context, here’s the post on this blog first introducing ShinyStan (formerly shinyStan) from earlier this year. ShinyStan v2.0.0 released ShinyStan v2.0.0 is now available on CRAN. This is […]

Fitting a multilevel model

Cui Yang writes: I have a question about the use of BRT (Boosting regression tree). I am planning to write an article about the effects of soil fauna and understory fine roots on forest soil organic carbon. The experiment was conducted in a subtropical forest area in China. There were 16 blocks each with 5 […]

Stan at JSM2015

In addition to Jigiang’s talk on Stan, 11:25 AM on Wednesday, I’ll also be giving a talk about Hamiltonian Monte Carlo today at 3:20 PM.  Stanimals in attendance can come find me to score a sweet Stan sticker. And everyone should check out Andrew’s breakout performance in “A Stan is Born”. Update: Turns out I missed even […]