Bayesian prediction with high-order interactions!!

Longhai Li did a really cool Ph.D. thesis (under the supervision of Radford Neal) on computing for models with deep interactions. The website containing all stuff about this software, including
the R packages, documentations and references, is here and here. Here's a quick description (from the website):

This R package is used in two situations. The first is to predict the next outcome based on the previous states of a discrete sequence. The second is to classify a discrete response based on a number of discrete covariates. In both situations, we use Bayesian logistic regression models that consider the high-order interactions. The time arising from using high-order interactions is reduced greatly by our compression technique that represents a group of original parameters as a single one in MCMC step. In this version, we use log-normal prior for the hyperparameters. When it is used for the second situation --- classification, we consider the full set of interaction patterns up to a specified order.

And here's the research paper (by Longhai and Radford). I wonder if they've achieved some of my goals in wanting weakly informative priors for models with interactions. That Cauchy thing rings a bell.

P.S. to Longhai: I don't recommend keeping your software in two places. Won't it be a pain to keep both sites up-to-date? Or maybe it's done automatically, I don't know.

Statistical Modeling, Causal Inference, and Social Science: Bayesian prediction with high-order interactions!!

Bayesian prediction with high-order interactions!!