PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Expectation Correction for an augmented class of Switching Linear Gaussian Models
David Barber
(2005) Technical Report. IDIAP Research Institute, Switzerland.

Abstract

We consider approximate inference in a class of switching linear Gaussian State Space models which includes the switching Kalman Filter and the more general case of switch transitions dependent on the continuous hidden state. The method is a novel form of Gaussian sum smoother consisting of a single forward and backward pass, and compares favourably against a range of competing techniques, including sequential Monte Carlo and Expectation Propagation.

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EPrint Type:Monograph (Technical Report)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Speech
Theory & Algorithms
ID Code:1990
Deposited By:David Barber
Deposited On:09 January 2006