Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation

Bayesian inference has found widespread application and use in science and engineering to reconcile Earth system models with data, including prediction in space (interpolation), prediction in time (forecasting), assimilation of observations and deterministic/stochastic model output, and inference of...

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Veröffentlicht in:	Environmental modelling & software : with environment data news Jg. 75; S. 273 - 316
1. Verfasser:	Vrugt, Jasper A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.01.2016
Schlagworte:	Adaptive metropolis (AM) algorithms Approximate Bayesian computation (ABC) Bayesian analysis Bayesian inference Bayesian model averaging (BMA) Bayesian theory case studies chemistry Computation Computer simulation computer software Diagnostic model evaluation differential equation Differential evolution Markov chain (DE-MC) Earth system science ecology engineering Environmental modeling environmental models Extended metropolis algorithm (EMA) Generalized likelihood uncertainty estimation (GLUE) geophysics hydrology Inference Likelihood function Markov chain Markov chain Monte Carlo (MCMC) simulation Markov chains Mathematical models Matlab Monte Carlo method Multi-processor computing physics Posterior distribution prediction Prior distribution Random walk metropolis (RWM) Residual analysis sampling scientists simulation models Prior distribution Approximate Bayesian computation (ABC) Markov chain Monte Carlo (MCMC) simulation Residual analysis Diagnostic model evaluation Environmental modeling Generalized likelihood uncertainty estimation (GLUE) Multi-processor computing Differential evolution Markov chain (DE-MC) Extended metropolis algorithm (EMA) Bayesian inference Adaptive metropolis (AM) Bayesian model averaging (BMA) Posterior distribution Random walk metropolis (RWM) Likelihood function
ISSN:	1364-8152
Online-Zugang:	Volltext
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