Chapter 3 Variational Bayesian learning of generative models (2008)
Harri Valpola, Antti Honkela, Er Ilin, Tapani Raiko, Markus Harva, Tomas Östman, ...
70 Variational Bayesian learning of generative models 3.1 Bayesian modeling and variational learning Unsupervised learning methods are often based on a generative approach where the goal is to find a...
Empirical evidence of the linear nature of magnetoencephalograms (2005)
Honkela, Antti, Östman, Tomas, Vigário, Ricardo
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...
Nonlinear independent factor analysis by hierarchical models (2003)
Harri Valpola, Tomas Östman, Juha Karhunen
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...
Missing Values in Hierarchical Nonlinear Factor Analysis (2003)
Tapani Raiko, Harri Valpola, Tomas Östman, Juha Karhunen
The properties of hierarchical nonlinear factor analysis (HNFA) recently introduced by Valpola and others [1] are studied by reconstructing missing values. The variational Bayesian learning algorithm...
Missing Values in Hierarchical Nonlinear Factor Analysis (2003)
Tapani Raiko, Harri Valpola, Tomas Östman, Juha Karhunen
The properties of hierarchical nonlinear factor analysis (HNFA) recently introduced by Valpola and others [1] are studied by reconstructing missing values. The variational Bayesian learning algorithm...
Missing values in hierarchical nonlinear factor analysis (2003)
Tapani Raiko, Harri Valpola, Tomas Östman, Juha Karhunen
The properties of hierarchical nonlinear factor analysis (HNFA) recently introduced by Valpola and others [1] are studied by reconstructing missing values. The variational Bayesian learning algorithm...