Title :
Estimation and Model Selection for an IDE-Based Spatio-Temporal Model
Author :
Scerri, Kenneth ; Dewar, Michael ; Kadirkamanathan, Visakan
Author_Institution :
Dept. of Syst. & Control Eng., Univ. of Malta, Msida
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.
Keywords :
Bayes methods; integro-differential equations; maximum likelihood estimation; signal sampling; Bayes factor; Bayesian model selection; IDE-based spatio-temporal model; data augmentation algorithm; maximum a posteriori sense; model selection; multidimensional sampling theory; parameter space; redistribution kernel; state space model; stochastic integro-difference equation; system spatial bandwidth; Bayes factor; data augmentation (DA) algorithm; dynamic spatio-temporal modeling; integro-difference equations; state-space models;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2008.2008550
