Emulation of Stochastic Simulators Using Generalized Lambda Models

نویسندگان

چکیده

Stochastic simulators are ubiquitous in many fields of applied sciences and engineering. In the context uncertainty quantification optimization, a large number simulations is usually necessary, which becomes intractable for high-fidelity models. Thus surrogate models stochastic have been intensively investigated last decade. this paper, we present novel approach to surrogating response distribution simulator uses generalized lambda distributions, whose parameters represented by polynomial chaos expansions model inputs. As opposed most existing approaches, new method does not require replicated runs at each point experimental design. We propose fitting procedure combines maximum conditional likelihood estimation with (modified) feasible least-squares. compare our state-of-the-art nonparametric kernel on four different applications stemming from mathematical finance epidemiology. Its performance illustrated terms accuracy both mean/variance distribution. proposed can also be used designs containing replications, carry out comparison two examples, showing that replications do necessarily help get better overall may even worsen results (at fixed total simulator).

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ژورنال

عنوان ژورنال: SIAM/ASA Journal on Uncertainty Quantification

سال: 2021

ISSN: ['2166-2525']

DOI: https://doi.org/10.1137/20m1337302