Name
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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TSP.2008.2008550 | IEEE Transactions on Signal Processing |
Keywords | Field | DocType |
importance sampling,state space model,kernel support,data augmentation algorithm,chosen dimensions result,candidate model structure,state space,bayesian model selection,ide-based spatio-temporal model,parameter space dimension,parameter space,bandwidth,maximum likelihood estimation,predictive models,bayes factor,model selection,organisms,kernel,monte carlo methods,difference equation | Mathematical optimization,Importance sampling,Bayesian inference,State-space representation,Bayes factor,Model selection,Maximum a posteriori estimation,Kernel method,State space,Mathematics | Journal |
Volume | Issue | ISSN |
57 | 2 | 1053-587X |
Citations | PageRank | References |
8 | 0.60 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenneth Scerri | 1 | 42 | 4.36 |
| M. Dewar | 2 | 64 | 3.76 |
| Visakan Kadirkamanathan | 3 | 431 | 62.00 |