Brendon Greeff writes: I was searching for an online math blog and found your email address. I have a question relating to the draw for a sports tournament. If there are 20 teams in a tournament divided into 4 groups, and those teams are selected based on four “bands” (Band: 1-5 ranked teams, 6-10, 11-15, […]

**Statistical computing**category.

## Stan *is* Turing Complete. So what?

This post is by Bob Carpenter. Stan is Turing complete! There seems to a persistent misconception that Stan isn’t Turing complete.1, 2 My guess is that it stems from Stan’s (not coincidental) superficial similarity to BUGS and JAGS, which provide directed graphical model specification languages. Stan’s Turing completeness follows from its support of array data […]

## Superfast Metrop using data partitioning, from Marco Banterle, Clara Grazian, and Christian Robert

Superfast not because of faster convergence but because they use a clever acceptance/rejection trick so that most of the time they don’t have to evaluate the entire target density. It’s written in terms of single-step Metropolis but I think it should be possible to do it in HMC or Nuts, in which case we could […]

## Bayesian nonparametric weighted sampling inference

Yajuan Si, Natesh Pillai, and I write: It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference using inverse-probability weights. We use a hierarchical approach in which we model the distribution of the weights of the nonsampled units in the […]

## WAIC and cross-validation in Stan!

Aki and I write: The Watanabe-Akaike information criterion (WAIC) and cross-validation are methods for estimating pointwise out-of-sample prediction accuracy from a fitted Bayesian model. WAIC is based on the series expansion of leave-one-out cross-validation (LOO), and asymptotically they are equal. With finite data, WAIC and cross-validation address different predictive questions and thus it is useful […]

## 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 (http://www.pnas.org/content/early/2014/04/29/1403623111.full.pdf) along with their response (http://www.pnas.org/content/early/2014/04/29/1404661111.full.pdf). 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: […]