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Archive of posts filed under the Stan category.

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 […]

Thanks, eBay!

Our recent Stan short course went very well, and we wanted to thank Julia Neznanova and Paul Burt of eBay NYC for giving us the space where we held the class.

Bayesian Inference with Stan for Pharmacometrics Class

Bob Carpenter, Daniel Lee, and Michael Betancourt will be teaching the 3-day class starting on 19 September in Paris. Following is the outline for the course: Day 1 Introduction to Bayesian statistics Likelihood / sampling distributions Priors, Posteriors via Bayes’s rule Posterior expectations and quantiles Events as expectations of indicator functions Introduction to Stan Basic […]

Bayesian Linear Mixed Models using Stan: A tutorial for psychologists, linguists, and cognitive scientists

This article by Tanner Sorensen, Sven Hohenstein, and Shravan Vasishth might be of interest to some of you.

One-day workshop on causal inference (NYC, Sat. 16 July)

James Savage is teaching a one-day workshop on causal inference this coming Saturday (16 July) in New York using RStanArm. Here’s a link to the details: One-day workshop on causal inference Here’s the course outline: How do prices affect sales? What is the uplift from a marketing decision? By how much will studying for an […]

Some insider stuff on the Stan refactor

From the stan-dev list, Bob wrote [and has since added brms based on comments; the * packages are ones that aren’t developed or maintained by the stan-dev team, so we only know what we hear from their authors]: The bigger picture is this, and you see the stan-dev/stan repo really spans three logical layers: stan […]

Reproducible Research with Stan, R, knitr, Docker, and Git (with free GitLab hosting)

Jon Zelner recently developed a neat Docker packaging of Stan, R, and knitr for fully reproducible research. The first in his series of posts (with links to the next parts) is here: * Reproducibility, part 1 The post on making changes online and auto-updating results using GitLab’s continuous integration service is here: * GitLab continuous […]

“Simple, Scalable and Accurate Posterior Interval Estimation”

Cheng Li, Sanvesh Srivastava, and David Dunson write: We propose a new scalable algorithm for posterior interval estimation. Our algorithm first runs Markov chain Monte Carlo or any alternative posterior sampling algorithm in parallel for each subset posterior, with the subset posteriors proportional to the prior multiplied by the subset likelihood raised to the full […]

Short course on Bayesian data analysis and Stan 18-20 July in NYC!

Jonah Gabry, Vince Dorie, and I are giving a 3-day short course in two weeks. Before class everyone should install R, RStudio and RStan on their computers. (If you already have these, please update to the latest version of R and the latest version of Stan, which is 2.10.) If problems occur please join the […]

Euro 2016 update

Big news out of Europe, everyone’s talking about soccer. Leo Egidi updated his model and now has predictions for the Round of 16: Here’s Leo’s report, and here’s his zipfile with data and Stan code. The report contains some ugly histograms showing the predictive distributions of goals to be scored in each game. The R […]

YouGov uses Mister P for Brexit poll

Ben Lauderdale and Doug Rivers give the story: There has been a lot of noise in polling on the upcoming EU referendum. Unlike the polls before the 2015 General Election, which were in almost perfect agreement (though, of course, not particularly close to the actual outcome), this time the polls are in serious disagreement. Telephone […]

Reduced-dimensionality parameterizations for linear models with interactions

After seeing this post by Matthew Wilson on a class of regression models called “factorization machines,” Aki writes: In a typical machine learning way, this is called “machine”, but it would be also a useful mode structure in Stan to make linear models with interactions, but with a reduced number of parameters. With a fixed […]

Log Sum of Exponentials for Robust Sums on the Log Scale

This is a public service announcement in the interest of more robust numerical calculations. Like matrix inverse, exponentiation is bad news. It’s prone to overflow or underflow. Just try this in R: > exp(-800) > exp(800) That’s not rounding error you see. The first one evaluates to zero (underflows) and the second to infinity (overflows). […]

Stan makes Euro predictions! (now with data and code so you can fit your own, better model)

Leonardo Egidi writes: Inspired by your world cup model I fitted in Stan a model for the Euro Cup which start today, with two Poisson distributions for the goals scored at every match by the two teams (perfect prediction for the first match!). Data and code are here. Here’s the model, and here are the […]

Betancourt Binge (Video Lectures on HMC and Stan)

Even better than binging on Netflix, catch up on Michael Betancourt’s updated video lectures, just days after their live theatrical debut in Tokyo. Scalable Bayesian Inference with Hamiltonian Monte Carlo (YouTube, 1 hour) Some Bayesian Modeling Techniques in Stan (YouTube, 1 hour 40 minutes) His previous videos have received very good reviews and they’re only […]

A Primer on Bayesian Multilevel Modeling using PyStan

Chris Fonnesbeck contributed our first PyStan case study (I wrote the abstract), in the form of a very nice Jupyter notebook. Daniel Lee and I had the pleasure of seeing him present it live as part of a course we were doing at Vanderbilt last week. A Primer on Bayesian Multilevel Modeling using PyStan This […]

Stan workshop this Thurs NYC

Jonah is speaking at the Bayesian Data Analysis meetup on Thursday night, “Stan Workshop. Life is precious: fix your sampling problems.” He’ll focus on common problems using MCMC and how to address them. For registration: