A comparison of two Bayesian approaches for uncertainty quantification

Statistical calibration of model parameters conditioned on observations is performed in a Bayesian framework by evaluating the joint posterior probability density function (pdf) of the parameters. The posterior pdf is very often inferred by sampling the parameters with Markov Chain Monte Carlo (MCMC...

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Bibliographic Details
Published in:	Environmental modelling & software : with environment data news Vol. 82; pp. 21 - 30
Main Authors:	Mara, Thierry A., Delay, Frederick, Lehmann, François, Younes, Anis
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.08.2016 Elsevier
Subjects:	algorithms Applications Bayesian analysis Bayesian parameter estimation Bayesian theory computer software DREAM(ZS) environmental models Markov chain Mathematical models MCMC MCPD sampler Parameter uncertainty Predictive uncertainty Probability density functions probability distribution Samplers Samples Soil hydraulic parameter identification soil hydraulic properties Statistical analysis Statistical methods Statistics Uncertainty Parameter uncertainty MCPD sampler MCMC Bayesian parameter estimation Predictive uncertainty DREAM(ZS) Soil hydraulic parameter identification soil hydraulic parameter identification predictive uncertainty parameter uncertainty DREAM_(ZS) MCMC
ISSN:	1364-8152
Online Access:	Get full text
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