Laboratory of Computer and Information Science / Neural Networks Research Centre CIS Lab Helsinki University of Technology

Bayesian algorithms for latent variable models

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.

Research of the Bayes group

Earlier research topics

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.

Group members

External collaborators

Former members

Dr. Markus Harva, Teemu Tiinanen, Matti Tornio, Dr. Harri Valpola, Tomas Östman, Leo Lundqvist, Juha Reunanen, and Xavier Giannakopoulos.

Previous research results

You can find information on the earlier research results of our Bayes group from the biennial reports covering the years 2006-2007, 2004-2005, 2002-2003, and 2000-2001.

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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