PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Data-Driven Spatio-Temporal Modeling Using the Integro-Difference Equation
Michael Dewar, Kenneth Scerri and visakan kadirkamanathan
IEEE Transactions on Signal Processing Volume 57, Number 1, pp. 83-91, 2009. ISSN 1053-587X

Abstract

A continuous-in-space, discrete-in-time dynamic spatio-temporal model known as the integro-difference equation (IDE) model is presented in the context of data-driven modeling. A novel decomposition of the IDE is derived, leading to state-space representation that does not couple the number of states with the number of observation locations or the number of parameters. Based on this state-space model, an expectation-maximization (EM) algorithm is developed in order to jointly estimate the IDE model's spatial field and spatial mixing kernel. The resulting modeling framework is demonstrated on a set of examples.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4653
Deposited By:Michael Dewar
Deposited On:13 March 2009