You’ll have to read the New Yorker article on Richard M. Stallman and the The GNU Manifesto by Maria Bustillos to find out! And what’s up with Tim O’Reilly’s comments about the Old Testment vs. New Testament? That’s an ad hominem attack of the highest order, guaranteed to get the Judeo-Christians even more riled […]
Jonah writes: First of all, every time I type ‘shinyapps’ the autocorrect replaces it with ‘chin-ups’. It was amusing but now it’s just annoying. You’d think Apple would have added the ability for the autocorrect to notice that I keep changing it back to “shinyapps” without making me manually add it as an exception. That’s […]
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).
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!
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 […]
If you’re in NYC or Sidney, there are some Stan-related talks in the next few weeks. New York 25 February. Jonah Gabry: shinyStan: a graphical user interface for exploring Bayesian models after MCMC. Register Now: New York Open Statistical Programming Meetup. 12 March. Rob Trangucci: #5: Non-centered parameterization aka the “Matt trick.” Register Now: Stan […]
Alp Kucukelbir, Rajesh Ranganath, Dave Blei, and I write: We describe an automatic variational inference method for approximating the posterior of differentiable probability models. Automatic means that the statistician only needs to define a model; the method forms a variational approximation, computes gradients using automatic differentiation and approximates expectations via Monte Carlo integration. Stochastic gradient […]
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 […]
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 […]
We’re happy to announce the release of Stan 2.6, including RStan, PyStan, CmdStan; it will also work with the existing Stan.jl and MatlabStan. Although there is some new functionality (hence the minor version bump), this is primarily a maintenance release. It fixes all of the known memory issues with Stan 2.5.0 and improves overall speed […]