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The building blocks introduced earlier by us in [1] are used for constructing a hierarchical nonlinear model for nonlinear factor analysis. We call the resulting method hierarchical nonlinear factor analysis (HNFA). The variational Bayesian learning algorithm used in this method has a linear computational complexity, and it is able to infer the structure of(More)
The properties of hierarchical nonlinear factor analysis (HNFA) recently introduced by Valpola and others [3] are studied by reconstructing values. The variational Bayesian learning algorithm for HNFA has linear computational complexity and is able to infer the structure of the model in addition to estimating the parameters. To compare HNFA with other(More)
Over recent years many algorithms have been used for the analysis of electro-and magnetoencephalograms, assuming a linear model for the mixing of cortical activity at the sensor plane. Such linearity can be theoretically justified, through the Maxwell equations. In this paper we exploit the adaptive and modular nature of the variational Bayesian(More)
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