PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Bayes Blocks: An Implementation of the Variational Bayesian Building Blocks Framework
Markus Harva, Tapani Raiko, Antti Honkela, Harri Valpola and Juha Karhunen
In: 21st Conference on Uncertainty in Artificial Intelligence, UAI 2005, 26-29 Jul, 2005, Edinburgh, Scotland.

Abstract

A software library for constructing and learning probabilistic models is presented. The library offers a set of building blocks from which a large variety of static and dynamic models can be built. These include hierarchical models for variances of other variables and many nonlinear models. The underlying variational Bayesian machinery, providing for fast and robust estimation but being mathematically rather involved, is almost completely hidden from the user thus making it very easy to use the library. The building blocks include Gaussian, rectified Gaussian and mixture-of-Gaussians variables and computational nodes which can be combined rather freely.

Postscript - PASCAL Members only - Requires a viewer, such as GhostView
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:1524
Deposited By:Tapani Raiko
Deposited On:28 November 2005