Skip to content
Archive of posts filed under the Statistical computing category.

Github cheat sheet

Mike Betancourt pointed us to this page. Maybe it will be useful to you too.

Lewis Richardson, father of numerical weather prediction and of fractals

Lee Sechrest writes: If you get a chance, Wiki this guy: I [Sechrest] did and was gratifyingly reminded that I read some bits of his work in graduate school 60 years ago. Specifically, about his math models for predicting wars and his work on fractals to arrive at better estimates of the lengths of common […]

Stan comes through . . . again!

Erikson Kaszubowski writes in: I missed your call for Stan research stories, but the recent post about stranded dolphins mentioned it again. When I read about the Crowdstorming project in your blog, I thought it would be a good project to apply my recent studies in Bayesian modeling. The project coordinators shared a big dataset […]

Expectation propagation as a way of life

Aki Vehtari, Pasi Jylänki, Christian Robert, Nicolas Chopin, John Cunningham, and I write: We revisit expectation propagation (EP) as a prototype for scalable algorithms that partition big datasets into many parts and analyze each part in parallel to perform inference of shared parameters. The algorithm should be particularly efficient for hierarchical models, for which the […]

Next Generation Political Campaign Platform?

[This post is by David K. Park] I’ve been imagining the next generation political campaign platform. If I were to build it, the platform would have five components: Data Collection, Sanitization, Storage, Streaming and Ingestion: This area will focus on the identification and development of the tools necessary to acquire the correct data sets for […]

Bayesian Cognitive Modeling Models Ported to Stan

Hats off for Martin Šmíra, who has finished porting the models from Michael Lee and Eric-Jan Wagenmakers’ book Bayesian Cognitive Modeling  to Stan. Here they are: Bayesian Cognitive Modeling: Stan Example Models Martin managed to port 54 of the 57 models in the book and verified that the Stan code got the same answers as […]

Soil Scientists Seeking Super Model

I (Bob) spent last weekend at Biosphere 2, collaborating with soil carbon biogeochemists on a “super model.” Model combination and expansion The biogeochemists (three sciences in one!) have developed hundreds of competing models and the goal of the workshop was to kick off some projects on putting some of them together intos wholes that are […]

Stan hits bigtime

First Wikipedia, then the Times (featuring Yair Ghitza), now Slashdot (featuring Allen “PyStan” Riddell). Just get us on Gawker and we’ll have achieved total media saturation. Next step, backlash. Has Stan jumped the shark? Etc. (We’d love to have a “jump the shark” MCMC algorithm but I don’t know if or when we’ll get there. […]

Just imagine if Ed Wegman got his hands on this program—it could do wonders for his research productivity!

Brendan Nyhan writes: I’d love to see you put some data in here that you know well and evaluate how the site handles it. The webpage in question says: Upload a data set, and the automatic statistician will attempt to describe the final column of your data in terms of the rest of the data. […]

“The Firth bias correction, penalization, and weakly informative priors: A case for log-F priors in logistic and related regressions”

Sander Greenland sent me this paper that he wrote with Mohammad Ali Mansournia, which discusses possible penalty functions for penalized maximum likelihood or, equivalently, possible prior distributions for Bayesian posterior mode estimation, in the context of logistic regression. Greenland and Mansournia write: We consider some questions that arise when considering alternative penalties . . . […]