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Sequential Monte-Carlo algorithms for Bayesian model calibration – A review and method comparison✰

Speich, Matthias ; Dormann, Carsten F. ; Hartig, Florian



Abstract

Bayesian inference has become an important framework for calibrating complex ecological and environmental models. Markov-Chain Monte Carlo (MCMC) algorithms are the methodological backbone of this framework, but they are not easily parallelizable and can thus not make optimal use of modern computer architectures. A possible solution is the use of Sequential Monte Carlo (SMC) algorithms. ...

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