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Bayesian methods in hydrologic modeling: A study of recent advancements in Markov chain Monte Carlo techniques
Author(s) -
Smith Tyler Jon,
Marshall Lucy Amanda
Publication year - 2008
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 - 10.1029/2007wr006705
Subject(s) - markov chain monte carlo , computer science , metropolis–hastings algorithm , initialization , markov chain , bayesian probability , monte carlo method , estimation theory , hydrological modelling , algorithm , mathematical optimization , machine learning , artificial intelligence , mathematics , statistics , climatology , programming language , geology
Bayesian methods, and particularly Markov chain Monte Carlo (MCMC) techniques, are extremely useful in uncertainty assessment and parameter estimation of hydrologic models. However, MCMC algorithms can be difficult to implement successfully because of the sensitivity of an algorithm to model initialization and complexity of the parameter space. Many hydrologic studies, even relatively simple conceptualizations, are hindered by complex parameter interactions where typical uncertainty methods are harder to apply. This paper presents comparisons between three recently introduced MCMC approaches, the adaptive Metropolis, the delayed rejection adaptive Metropolis, and the differential evolution Markov chain algorithms via two case studies: (1) a synthetic Gaussian mixture with five parameters and two modes and (2) a real‐world hydrologic modeling scenario where each algorithm will serve as the uncertainty and parameter estimation framework for a conceptual precipitation‐runoff model.