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Spatio‐temporal processes

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Abstract We present an overview of the literature on the analysis of spatio‐temporal processes with a nonseparable covariance structure. We focus on those methods that rely heavily on computing for the estimation or inference. Topics are classified into frequentist approaches, which rely on expectation–maximization algorithms, and hierarchical Bayesian approaches, which rely on Markov chain Monte Carlo. We also present discussions on other computational issues related to the analysis of spatio‐temporal data. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data

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Data: Types and Structure > Time Series, Stochastic Processes, and Functional Data

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