Parameter optimization and uncertainty assessment for rainfall frequency modeling using an adaptive Metropolis–Hastings algorithm

A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis–Hastings (AM-H) algorithm is presented for extreme rainfall frequency modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of...

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Autores principales: Xingpo Liu, Chengfei Xia, Yifan Tang, Jiayang Tu, Huimin Wang
Formato: article
Lenguaje:EN
Publicado: IWA Publishing 2021
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Acceso en línea:https://doaj.org/article/9d38a50752a74a68828bf7e3b4434d7a
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