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Archive of posts filed under the Statistical computing category.

An interesting mosaic of a data programming course

Rajit Dasgupta writes: I have been working on a website, SlideRule that in its present state, is a catalog of online courses aggregated from over 35 providers. One of the products we are building on top of this is something called Learning Paths, which are essentially a sequence of Online Courses designed to help learners […]

Thermodynamic Monte Carlo: Michael Betancourt’s new method for simulating from difficult distributions and evaluating normalizing constants

I hate to keep bumping our scheduled posts but this is just too important and too exciting to wait. So it’s time to jump the queue. The news is a paper from Michael Betancourt that presents a super-cool new way to compute normalizing constants: A common strategy for inference in complex models is the relaxation […]

“The results (not shown) . . .”

Pro tip: Don’t believe any claims about results not shown in a paper. Even if the paper has been published. Even if it’s been cited hundreds of times. If the results aren’t shown, they haven’t been checked. I learned this the hard way after receiving this note from Bin Liu, who wrote: Today I saw […]

Once more on nonparametric measures of mutual information

Ben Murell writes: Our reply to Kinney and Atwal has come out ( along with their response ( I feel like they somewhat missed the point. If you’re still interested in this line of discussion, feel free to post, and maybe the Murrells and Kinney can bash it out in your comments! Background: Too many […]

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. […]