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A Bayesian approach is developed to analyze change points in multivariatetime series and space-time data. The methodology is used to assess the impactof extended inundation on the ecosystem of the Gulf Plains bioregion innorthern Australia. The proposed approach can be implemented for dynamicmixture models that have a conditionally Gaussian state space representation.Details are given on how to efficiently implement the algorithm for a generalclass of multivariate time series and space-time models. This efficientimplementation makes it feasible to analyze high dimensional, but of realisticsize, space-time data sets because our approach can be appreciably faster,possibly millions of times, than a standard implementation in such cases.

A Bayesian changepoint methodology for high dimensional multivariate  time series and space-time data: A study of structural change using remotely  sensed data