Bayesian Extremes: Rishikesh Yadav
From Belle Taylor
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Name: Rishikesh Yadav
Talk Title: A flexible Bayesian framework for modeling extreme spatial threshold exceedances using product mixtures of random fields
Abstract: In this work, we develop a constructive modeling framework for extreme spatial threshold exceedances, based on general product mixtures of random fields possessing light or heavy-tailed margins and various spatial dependence characteristics, which are suitably designed to provide high flexibility in the tail and at pre-asymptotic levels. Our proposed model is akin to a recently proposed Gamma-Gamma model, but it possesses a higher degree of flexibility in its joint tail structure, capturing strong dependence more easily. Thanks to the model’s hierarchical formulation, inference may be conveniently performed based on Markov chain Monte Carlo (MCMC) methods, and we demonstrate how the stochastic gradient Langevin dynamics (SGLD) algorithm can be exploited to fit our proposed model very efficiently in relatively big spatio-temporal dimensions despite the large number of latent parameters, while simultaneously censoring non-threshold exceedances and performing spatial prediction at multiple sites. We explore the theoretical properties of the proposed model, and illustrate the proposed methodology by simulation and application to daily precipitation data from Eastern Spain measured at about 100 stations over the period 2011-2020
This talk is a contributed talk at EVA 2021.