Skip to content
Archive of posts filed under the Bayesian Statistics category.

My talk tomorrow (Thurs) at MIT political science: Recent challenges and developments in Bayesian modeling and computation (from a political and social science perspective)

It’s 1pm in room E53-482. I’ll talk about the usual stuff (and some of this too, I guess).

One simple trick to make Stan run faster

Did you know that Stan automatically runs in parallel (and caches compiled models) from R if you do this: source(“http://mc-stan.org/rstan/stan.R”) It’s from Stan core developer Ben Goodrich. This simple line of code has changed my life. A factor-of-4 speedup might not sound like much, but, believe me, it is!

Introducing shinyStan

As a project for Andrew’s Statistical Communication and Graphics graduate course at Columbia, a few of us (Michael Andreae, Yuanjun Gao, Dongying Song, and I) had the goal of giving RStan’s print and plot functions a makeover. We ended up getting a bit carried away and instead we designed a graphical user interface for interactively exploring virtually […]

Bayes and doomsday

Ben O’Neill writes: I am a fellow Bayesian statistician at the University of New South Wales (Australia).  I have enjoyed reading your various books and articles, and enjoyed reading your recent article on The Perceived Absurdity of Bayesian Inference.  However, I disagree with your assertion that the “doomsday argument” is non-Bayesian; I think if you read […]

Statistical Significance – Significant Problem?

John Carlin, who’s collaborated on some of my recent work on Type S and Type M errors, prepared this presentation for a clinical audience. It might be of interest to some of you. The ideas and some of the examples should be familiar to regular readers of this blog, but it could be useful to […]

Bayesian survival analysis with horseshoe priors—in Stan!

Tomi Peltola, Aki Havulinna, Veikko Salomaa, and Aki Vehtari write: This paper describes an application of Bayesian linear survival regression . . . We compare the Gaussian, Laplace and horseshoe shrinkage priors, and find that the last has the best predictive performance and shrinks strong predictors less than the others. . . . And here’s […]

Stan Down Under

I (Bob, not Andrew) am in Australia until April 30. I’ll be giving some Stan-related and some data annotation talks, several of which have yet to be concretely scheduled. I’ll keep this page updated with what I’ll be up to. All of the talks other than summer school will be open to the public (the […]

Why I keep talking about “generalizing from sample to population”

Someone publishes some claim, some statistical comparison with “p less than .05″ attached to it. My response is: OK, you see this pattern in the sample. Do you think it holds in the population? Why do I ask this? Why don’t I ask the more standard question: Do you really think this result is statistically […]

Total survey error

Erez Shalom writes: It’s election time in Israel and every week several surveys come out trying to predict the ‘mandates’ that each party will get (out of a total of 120). These surveys are historically flakey, and no one takes the ‘sampling error’ they come with seriously, but no one has a good idea of […]

This has nothing to do with the Super Bowl

Joshua Vogelstein writes: The Open Connectome Project at Johns Hopkins University invites outstanding candidates to apply for a postdoctoral or assistant research scientist position in the area of statistical machine learning for big brain imaging data. Our workflow is tightly vertically integrated, ranging from raw data to theory to answering neuroscience questions and back again. […]