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A Comparison of Approximate Bayesian Computation and Stochastic Calibration for Spatio-Temporal Models of High-Frequency Rainfall Patterns

Presented by: 
Matthew Pratola Ohio State University
Thursday 12th April 2018 - 15:30 to 16:00
INI Seminar Room 1
Modeling complex environmental phenomena such as rainfall patterns has proven challenging due to the difficulty in capturing heavy-tailed behavior, such as extreme weather, in a meaningful way. Recently, a novel approach to this task has taken the form of so-called stochastic weather generators, which use statistical formulations to emulate the distributional patterns of an environmental process. However, while sampling from such models is usually feasible, they typically do not possess closed-form likelihood functions, rendering the usual approaches to model fitting infeasible. Furthermore, some of these stochastic weather generators are now becoming so complex that even simulating from them can be computationally expensive. We propose and compare two approaches to fitting computationally expensive stochastic weather generators motivated by Approximate Bayesian Computation and Stochastic Simulator Calibration methodologies. The methods are then demonstrated by estimating important parameters of a recent stochastic weather generator model applied to rainfall data from the continental USA.
University of Cambridge Research Councils UK
    Clay Mathematics Institute London Mathematical Society NM Rothschild and Sons