A Consistent Bayesian Inference Problem with Time Series Data

Abstract

To quantify uncertainties of inputs to models of dynamical systems, a fixed spatial configuration of sensors is often designed and deployed to collect time-series data for solving stochastic inverse problems. A general goal is to configure the sensors so that they provide useful information for the stochastic inverse problem over the full duration of the experiment and provide us with minimally redundant data. We use a recently developed Consistent Bayesian framework for formulating and solving stochastic inverse problems to investigate the effects of measurement frequency, duration, and quality on posterior distributions and predictions.

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Portland, Oregon
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Michael Pilosov
(applied) math nerd on a mission.
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