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Archive of posts filed under the Statistical computing category.

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 […]

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. […]

Six quick tips to improve your regression modeling

It’s Appendix A of ARM: A.1. Fit many models Think of a series of models, starting with the too-simple and continuing through to the hopelessly messy. Generally it’s a good idea to start simple. Or start complex if you’d like, but prepare to quickly drop things out and move to the simpler model to help […]

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 […]