PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimation and Model Selection for an IDE-Based Spatio-Temporal Model
Kenneth Scerri, Michael Dewar and visakan kadirkamanathan
IEEE Transactions on Signal Processing Volume 57, Number 2, pp. 482-492, 2009. ISSN 1053-587X

Abstract

A state space model of the stochastic spatio-temporal Integro-Difference Equation (IDE) is derived. Based on multidimensional sampling theory, the dimensions of the state space and parameter space of the model are identified from the spatial bandwidth of the system and the support of the redistribution kernel of the IDE. When both the bandwidth and the kernel support are unknown, a method to propose a number of state space and parameter space dimensions is presented. These chosen dimensions result in a number of candidate model structures. Bayesian model selection, making use of Bayes factor, the data augmentation algorithm and importance sampling, is then used to identify the model best suited to represent the data in a maximum a posteriori sense.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:4651
Deposited By:Michael Dewar
Deposited On:13 March 2009