A range of inference methodologies are in use for spatio-temporal data, and the complexity and nature of each problem dictates the nature of the methodology used. The high dimensionality of spatio-temporal data often means inference methodologies exhibit a combination of model simplifications, approximations of estimators and likelihood functions, and/or computational and algorithmic efficiencies. Unlike many other ‘big data’ problems, however, an understanding of the physical properties of the spatio-temporal process in question often enables many simplifying assumptions to be made which lead to convenient and enabling mathematical properties (i.e.\ Markov property, stationarity, various forms of conditional independence). The workshop will be comprised of talks having a common emphasis on computational tractability and an accommodation of large, high resolution spatio-temporal datasets. Interactions with subject-area specialists in some of the application areas will also feature prominently in the workshop, through presentations, but also through panel discussions and roundtables.
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