We have studied variational Bayesian learning and applied it to various latent variable model structures which are suited to unsupervised learning. (See glossary for explanation of terms). We are mainly interested in models with continuous variables.
NEW! The Bayes chapter of the biennial report of our Adaptive Informatics Research Centre covering the years 2008-2009 contains extended summaries on our recent research results with references.
On our publication page, you can find the most important publications of our Bayes group downloadable in electronic form.
We have also a complete list of publications. Most of them can be downloaded in electronic form, too.
Here you can find free software packages prepared by the Bayes group. They include Bayes blocks and their extensions, programmed in C++ and Python, as well as Matlab packages for nonlinear factor analysis and nonlinear dynamical factor analysis.
Dr. Markus Harva, Teemu Tiinanen, Matti Tornio, Dr. Harri Valpola, Tomas Östman, Leo Lundqvist, Juha Reunanen, and Xavier Giannakopoulos.
Some of our research results have been described under the activities of the Independent component analysis (ICA) group of HUT, which studies ICA, blind source separation (BSS), and their extensions. For more detailed information, see the research reports of our ICA group covering the years 2006-2007, 2004-2005, and 2002-2003, as well as theoretical ICA research in 2000-2001, and applications of ICA in 2000-2001.
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