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Archive of posts filed under the Statistical computing category.

Transitioning to Stan

Kevin Cartier writes: I’ve been happily using R for a number of years now and recently came across Stan. Looks big and powerful, so I’d like to pick an appropriate project and try it out. I wondered if you could point me to a link or document that goes into the motivation for this tool […]

References (with code) for Bayesian hierarchical (multilevel) modeling and structural equation modeling

A student writes: I am new to Bayesian methods. While I am reading your book, I have some questions for you. I am interested in doing Bayesian hierarchical (multi-level) linear regression (e.g., random-intercept model) and Bayesian structural equation modeling (SEM)—for causality. Do you happen to know if I could find some articles, where authors could […]

The maximal information coefficient

Justin Kinney writes: I wanted to let you know that the critique Mickey Atwal and I wrote regarding equitability and the maximal information coefficient has just been published. We discussed this paper last year, under the heading, Too many MC’s not enough MIC’s, or What principles should govern attempts to summarize bivariate associations in large […]

Stan Model of the Week: PK Calculation of IV and Oral Dosing

[Update: Revised given comments from Wingfeet, Andrew and germo. Thanks! I'd mistakenly translated the dlnorm priors in the first version --- amazing what a difference the priors make. I also escaped the less-than and greater-than signs in the constraints in the model so they're visible. I also updated to match the thin=2 output of JAGS.] […]

Running into a Stan Reference by Accident

We were talking about parallelizing MCMC and I came up with what I thought was a neat idea for parallelizing MCMC (sample with fractional prior, average samples on a per-draw basis). But then I realized this approach could get the right posterior mean or right posterior variance, but not both, depending on how the prior […]

How to think about “identifiability” in Bayesian inference?

We had some questions on the Stan list regarding identification. The topic arose because people were fitting models with improper posterior distributions, the kind of model where there’s a ridge in the likelihood and the parameters are not otherwise constrained. I tried to help by writing something on Bayesian identifiability for the Stan list. Then […]

Special discount on Stan! $999 cheaper than Revolution R!

And we’ll throw in RStan and PyStan for free! Details here.

Stupid R Tricks: Random Scope

Andrew and I have been discussing how we’re going to define functions in Stan for defining systems of differential equations; see our evolving ode design doc; comments welcome, of course. About Scope I mentioned to Andrew I would prefer pure lexical, static scoping, as found in languages like C++ and Java. If you’re not familiar […]

My recent debugging experience

OK, so this sort of thing happens sometimes. I was working on a new idea (still working on it; if it ultimately works out—or if it doesn’t—I’ll let you know) and as part of it I was fitting little models in Stan, in a loop. I thought it would make sense to start with linear […]

2013

There’s lots of overlap but I put each paper into only one category.  Also, I’ve included work that has been published in 2013 as well as work that has been completed this year and might appear in 2014 or later.  So you can can think of this list as representing roughly two years’ work. Political […]