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

**Stan**category.

## 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 on the beach

This came in the email one day: We have used the great software Stan to estimate bycatch levels of common dolphins (Delphinus delphis) in the Bay of Biscay from stranding data. We found that official estimates are underestimated by a full order of magnitude. We conducted both a prior and likelihood sensitivity analyses : the […]

## Gary Venter’s age-period-cohort decomposition of US male mortality trends

Following up on yesterday’s post on mortality trends, I wanted to share with you a research note by actuary Gary Venter, “A Quick Look at Cohort Effects in US Male Mortality.” Venter produces this graph: And he writes: Cohort effects in mortality tend to be difficult to explain. Often strings of coincidences are invoked – […]

## Lots of buzz regarding this postdoc position in London

Tom Churcher writes: We are currently advertising for an infectious disease modeller to investigate the impact of insecticide resistance on malaria control in Africa. The position is for 3 years in the first instance and is funded by the Wellcome Trust. No previous malaria or mosi experience required. Please circulate to anyone who might be […]

## 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 […]