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- Description:
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
Other available revisons
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Version Changelog Date 1.04 In addition to the existing MATLAB implementation, the toolbox now also contains an R package of the variational Bayesian algorithm for mixed-effects inference.
March 26, 2013, 12:42:04 1.01 In addition to the existing MATLAB implementation, the toolbox now also contains an R package of the variational Bayesian algorithm for mixed-effects inference.
May 14, 2012, 11:36:06
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