Project details for MICP

Screenshot MICP 1.01

by kay_brodersen - June 1, 2012, 04:01:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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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.

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.

The toolbox provides (i) asymptotically exact MCMC implementations as well as (ii) computationally efficient variational Bayes approximations.

Changes to previous version:

In addition to the existing MATLAB implementation, the toolbox now also contains an R package of the variational Bayesian algorithm for mixed-effects inference.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
Supported Operating Systems: Platform Independent
Data Formats: Any
Tags: Matlab, Classifiaction, Accuracy, Balanced Accuracy, Bayesian Inference, Hierarchical Models, Mcmc, Shrinkage
Archive: download here


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