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Bayesian Adaptive Design for State-space Models with Covariates

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Design and Analysis of Experiments

Modelling data that change over space and time is important in many areas, such as environmental monitoring of air and noise pollution using a sensor network over a long period of time. Often such data are collected dynamically together with the values of a variety of related variables. Due to resource limitations, an optimal choice (or design) for the locations of the sensors is important for achieving accurate predictions. This choice depends on the adopted model, that is, the spatial and temporal processes, and the dependence of the responses on relevant covariates. We investigate adaptive designs for state-space models where the selection of locations at time point $t_{n+1}$ draws on information gained from observations made at the locations sampled at preceding time points $t_1, ldots, t_n$. A Bayesian design selection criterion is developed and its performance is evaluated using several examples.

This talk is part of the Isaac Newton Institute Seminar Series series.

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