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 […]

**Statistical computing**category.

## 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 […]

## 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). […]

## 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 […]

## Birthday analysis—Friday the 13th update, and some model checking

Carl Bialik and Andrew Flowers at fivethirtyeight.com (Nate Silver’s site) ran a story following up on our birthdays example—that time series decomposition of births by day, which is on the cover of the third edition of Bayesian Data Analysis using data from 1968-1988, and which then Aki redid using a new dataset from 2000-2014. Friday […]

## Point summary of posterior simulations?

Luke Miratrix writes: In the applied stats class I’m teaching on hierarchical models I’m giving the students (a mix of graduate students, many from the education school, and undergrads) a taste of Stan. I have to give them some “standard” way to turn Stan output into a point estimate (though of course I’ll also explain […]

## Stochastic natural-gradient EP

Yee Whye Teh sends along this paper with Leonard Hasenclever, Thibaut Lienart, Sebastian Vollmer, Stefan Webb, Balaji Lakshminarayanan, and Charles Blundell. I haven’t read it in detail but they not similarities to our “expectation propagation as a way of life” paper. But their work is much more advanced than ours.

## Stan Coding Corner: O(N) Change-Point Program with Clever Forward-Backward Calculation

It’s so much fun to work in open source. Luke Wiklendt sent along this improved code for a change-point model calculation in Stan. With N data points in the time series, the version in the manual is O(N2), whereas the improved version is O(N). In practice, Luke says [the new code] results in a dramatic […]

## Black Box Challenge

Georgy Cheremovskiy writes: I’m one of the organizers of an unusual reinforcement learning competition named Black Box Challenge. The conception is simple — one need to program an agent that can play a game with unknown rules. At each time step agent is given an environment state vector and has a few possible actions. The […]

## “Rbitrary Standards”

Allen and Michael pointed us on the Stan list to these amusing documents by Oliver Keyes: Rbitrary Standards: “This is an alternate FAQ for R. Specifically, it’s an FAQ that tries to answer all the questions about R’s weird standards, formatting and persnicketiness that you’re afraid to ask.” Parallelism, R, and OpenMP Enjoy.

## Actually, I’d just do full Bayes

Dave Clark writes: I was hoping for your opinion on a topic related to hierarchical models. I am an actuary and have generally worked with the concept of hierarchical models in the context of credibility theory. The text by Bühlmann and Gisler (A Course in Credibility Theory; Springer) sets up the mixed models under the […]

*Stan Case Studies* Launches

There’s a new section of the Stan web site, with case studies meant to illustrate statistical methodologies, classes of models, application areas, statistical computation, and Stan programming. Stan Case Studies The first ten or so are up, including a grab bag of education models from Daniel Furr at U.C. Berkeley: Hierarchical Two-Parameter Logistic Item Response […]

## Bayesian inference for network links

A colleague writes: I’m working with a doctoral student on a latent affinity network problem and we keep hitting challenges in sampling, in our case using Metropolis-Hastings, for the network links. As you can imagine, lots of local modes, things get stuck, etc . . . Any suggestions on how to sample network links? My […]