
 Description:
The glmie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glmie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x.
Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference.
 Changes to previous version:
New matrix class
Bugfixes
More examples
New penalty and potential functions
Group sparsity support
 BibTeX Entry: Download
 URL: Project Homepage
 Supported Operating Systems: Agnostic, Platform Independent
 Data Formats: Matlab, Octave
 Tags: Approximate Inference, Sparse Learning, Logistic Regression
 Archive: download here
Other available revisons

Version Changelog Date 1.5 added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes
generalised nonGaussian potentials so that affine instead of linear functions of the latent variables can be used
August 31, 2013, 22:38:05 1.4 contributed by George Papandreou:
preconditioning support in the inf/linsolve_lcg.m CG routine.
@matConv2 and @matFD2 support different boundary conditions in deblurring
various mat/@*/diagFAtAFt.m support circulant preconditioning
bugfixes in nonnegativity option in pls/plsLBFGS.m and pen/penVBNorm.m when used together with EP
inf/diag_inv_sample.m, a Monte Carlo estimator
gfortran support to pls/lbfgsb/Makefile (thanks to Ernst Kloppenburg)
slight modification to mat/@matFFTN/mvm.m to make it more consistent
simple gradient solver using BarzilaiBorwein step size pls/plsBB.m
October 19, 2011, 22:26:05 1.3 extension of the matrix class; bugfixes
plsSB  split Bregmin PLS solver
new inference engine dli.m instead of vbidl.m
support of parallel EP in addition to VB
marginal likelihood computation
extended documentation and demo programs
November 12, 2010, 09:57:36 1.2 New matrix class
Bugfixes
More examples
New penalty and potential functions
Group sparsity support
August 27, 2010, 11:27:27 1.1 Added demo examples and documentation.
August 10, 2010, 09:05:48 1.0 Initial Announcement on mloss.org.
August 10, 2010, 08:48:20 v1.0 Initial Announcement on mloss.org.
August 10, 2010, 08:29:47
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