PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Building Blocks for Variational Bayesian Learning of Latent Variable Models
Tapani Raiko, Harri Valpola, Markus Harva and Juha Karhunen
Journal of Machine Learning Research Volume 8, pp. 155-201, 2007.

Abstract

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 for variances and means. The test results demonstrate the good performance and usefulness of the introduced method.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3356
Deposited By:Tapani Raiko
Deposited On:09 February 2008