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 […]

**Bayesian Statistics**category.

## Informative priors for treatment effects

Biostatistician Garnett McMillan writes: A PI recently completed a randomized trial where the experimental treatment showed a large, but not quite statistically significant (p=0.08) improvement over placebo. The investigators wanted to know how many additional subjects would be needed to achieve significance. This is a common question, which is very hard to answer for non-statistical […]

## 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 […]

## My talk tomorrow (Thurs) 10:30am at ICML in NYC

I’ll be speaking at the workshop on Data-Efficient Machine Learning. And here’s the schedule. I’ll be speaking on the following topic: Toward Routine Use of Informative Priors Bayesian statistics is typically performed using noninformative priors but the resulting inferences commonly make no sense and also can lead to computational problems as algorithms have to waste […]

## 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 […]

## The answer is the Edlin factor

Garnett McMillan writes: You have argued about the pervasive role of the Garden of Forking Paths in published research. Given this influence, do you think that it is sensible to use published research to inform priors in new studies? My reply: Yes, I think you can use published research but in doing so you should […]

## 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 […]

## Freak Punts on Leicester Bet

I went over to the Freakonomics website and found this story about Leicester City’s unexpected championship. Here’s Stephen Dubner: At the start of this season, British betting houses put Leicester’s chances of winning the league at 5,000-to-1, which seemed, if anything, perhaps too generous. My [Dubner’s] son Solomon again: SOLOMON DUBNER: What would you say […]

## 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 […]

## Nick and Nate and Mark on Leicester and Trump

Just following up on our post the other day on retrospective evaluations of probabilistic predictions: For more on Leicester City, see Nick Goff on Why did bookmakers lose on Leicester? and What price SHOULD Leicester have been? (forwarded to me by commenter Iggy). For more on Trump, see Nate Silver on How I Acted Like […]

## 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 […]

## Bill James does model checking

Regular readers will know that Bill James was one of my inspirations for becoming a statistician. I happened to be browsing through the Bill James Historical Baseball Abstract the other day and came across this passage on Glenn Hubbard, who he ranks as the 88th best second baseman of all time: Total Baseball has Glenn […]