MICPhttp://mloss.orgUpdates and additions to MICPenTue, 26 Mar 2013 12:42:04 -0000MICP 1.04<html><p>Classification algorithms are often used in a hierarchical setting, where a classifier is trained and tested on individual datasets which are themselves sampled from a group. Examples of this sort of analysis are ubiquitous and are common in domains as varied as spam detection, brain-machine interfaces, and neuroimaging. </p> <p>This toolbox provides answers to the questions of statistical inference that arise in all of these settings. It implements models that account for both within-subjects (fixed-effects) and between-subjects (random-effects) variance components and thus provide mixed-effects inference. </p> <p>The toolbox provides (i) asymptotically exact MCMC implementations as well as (ii) computationally efficient variational Bayes approximations. </p></html>Kay H. BrodersenTue, 26 Mar 2013 12:42:04 -0000 accuracybayesian inferencehierarchical modelsmcmcshrinkage