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Data-Driven Spatio-Temporal Modeling Using the Integro-Difference Equation

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3 Author(s)
Dewar, M. ; Sch. of Inf., Univ. of Edinburgh, Edinburgh ; Scerri, K. ; Kadirkamanathan, V.

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.

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Signal Processing, IEEE Transactions on  (Volume:57 ,  Issue: 1 )