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Archive of posts filed under the Statistical computing category.

Stan (& JAGS) Tutorial on Linear Mixed Models

Shravan Vasishth sent me an earlier draft of this tutorial he co-authored with Tanner Sorensen. I liked it, asked if I could blog about it, and in response, they’ve put together a convenient web page with links to the tutorial PDF, JAGS and Stan programs, and data: Fitting linear mixed models using JAGS and Stan: […]

Discovering general multidimensional associations

Continuing our discussion of general measures of correlations, Ben Murrell sends along this paper (with corresponding R package), which begins: When two variables are related by a known function, the coefficient of determination (denoted R-squared) measures the proportion of the total variance in the observations that is explained by that function. This quantifies the strength […]

Heller, Heller, and Gorfine on univariate and multivariate information measures

Malka Gorfine writes: We noticed that the important topic of association measures and tests came up again in your blog, and we have few comments in this regard. It is useful to distinguish between the univariate and multivariate methods. A consistent multivariate method can recognise dependence between two vectors of random variables, while a univariate […]

Bayesian Uncertainty Quantification for Differential Equations!

Mark Girolami points us to this paper and software (with Oksana Chkrebtii, David Campbell, and Ben Calderhead). They write: We develop a general methodology for the probabilistic integration of differential equations via model based updating of a joint prior measure on the space of functions and their temporal and spatial derivatives. This results in a […]

Sleazy sock puppet can’t stop spamming our discussion of compressed sensing and promoting the work of Xiteng Liu

Some asshole who has a bug up his ass about compressed sensing is spamming our comments with a bunch of sock puppets. All from the same IP address: “George Stoneriver,” Scott Wolfe,” and just plain “Paul,” all saying pretty much the same thing in the same sort of broken English (except for Paul, whose post […]

Stan Model of the Week: Hierarchical Modeling of Supernovas

The Stan Model of the Week showcases research using Stan to push the limits of applied statistics.  If you have a model that you would like to submit for a future post then send us an email. Our inaugural post comes from Nathan Sanders, a graduate student finishing up his thesis on astrophysics at Harvard. […]

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.] […]