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Bayesian calibration and uncertainty analysis of hydrological models: A comparison of adaptive Metropolis and sequential Monte Carlo samplers
Author(s) -
Jeremiah Erwin,
Sisson Scott,
Marshall Lucy,
Mehrotra Rajeshwar,
Sharma Ashish
Publication year - 2011
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/2010wr010217
Subject(s) - markov chain monte carlo , metropolis–hastings algorithm , monte carlo method , robustness (evolution) , computer science , sampling (signal processing) , bayesian inference , importance sampling , posterior probability , mathematical optimization , bayesian probability , algorithm , mathematics , statistics , artificial intelligence , biochemistry , chemistry , filter (signal processing) , computer vision , gene
Bayesian statistical inference implemented by stochastic algorithms such as Markov chain Monte Carlo (MCMC) provides a flexible probabilistic framework for model calibration that accounts for both model and parameter uncertainties. The effectiveness of such Monte Carlo algorithms depends strongly on the user‐specified proposal or sampling distribution. In this article, a sequential Monte Carlo (SMC) approach is used to obtain posterior parameter estimates of a conceptual hydrologic model using data from selected catchments in eastern Australia. The results are evaluated against the popular adaptive Metropolis MCMC sampling approach. Both methods display robustness and convergence, but the SMC displays greater efficiency in exploring the parameter space in catchments where the optimal solutions lie in the tails of the prescribed prior distribution. The SMC method is also able to identify a different set of parameters with an overall improvement in likelihood and Nash‐Sutcliffe efficiency for selected catchments. As a result of its population‐based sampling mechanism, the SMC method is shown to offer improved efficiency in identifying parameter optimization and to provide sampling robustness, in particular in identifying global posterior modes.