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JMLR
2007

Building Blocks for Variational Bayesian Learning of Latent Variable Models

9 years 11 months ago
Building Blocks for Variational Bayesian Learning of Latent Variable Models
We introduce standardised building blocks designed to be used with variational Bayesian learning. The blocks include Gaussian variables, summation, multiplication, nonlinearity, and delay. A large variety of latent variable models can be constructed from these blocks, including nonlinear and variance models, which are lacking from most existing variational systems. The introduced blocks are designed to fit together and to yield efficient update rules. Practical implementation of various models is easy thanks to an associated software package which derives the learning formulas automatically once a specific model structure has been fixed. Variational Bayesian learning provides a cost function which is used both for updating the variables of the model and for optimising the model structure. All the computations can be carried out locally, resulting in linear computational complexity. We present experimental results on several structures, including a new hierarchical nonlinear model ...
Tapani Raiko, Harri Valpola, Markus Harva, Juha Ka
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2007
Where JMLR
Authors Tapani Raiko, Harri Valpola, Markus Harva, Juha Karhunen
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