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Author(s):
Schmidl, D.; Czado, C.; Hug, S.; Theis, F. J. 
Title:
A vine-copula based adaptive MCMC sampler for efficient inference of dynamical systems 
Abstract:
Statistical inference in high dimensional dynamical systems is often hindered by the unknown dependency structure of model parameters. In particu- lar, the inference of parameterized differential equations (DEs) via Markov chain Monte Carlo (MCMC) samplers often suffers from high proposal rejection rates and is exacerbated by strong autocorrelation structures within the Markov chains leading to poor mixing properties. In this paper, we develop a novel vine-copula based adaptive MCMC approach for...    »
 
Keywords:
Parameter inference Metropolis-Hastings algorithm Independence sampling Adaptive MCMC Vine Copula 
Journal title:
Bayesian Anal. 
Year:
2013 
Journal volume:
Journal issue:
Pages contribution:
1-22