Comparison study between MCMC-based and weight-based Bayesian methods for identification of joint distribution |
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Authors: | Yoojeong Noh K K Choi Ikjin Lee |
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Affiliation: | (1) Department of Mechanical & Industrial Engineering, College of Engineering, The University of Iowa, Iowa City, IA 52242, USA |
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Abstract: | The Bayesian method is widely used to identify a joint distribution, which is modeled by marginal distributions and a copula.
The joint distribution can be identified by one-step procedure, which directly tests all candidate joint distributions, or
by two-step procedure, which first identifies marginal distributions and then copula. The weight-based Bayesian method using
two-step procedure and the Markov chain Monte Carlo (MCMC)-based Bayesian method using one-step and two-step procedures were
recently developed. In this paper, the one-step weight-based Bayesian method and two-step MCMC-based Bayesian method using
the parametric marginal distributions are proposed. Comparison studies among the Bayesian methods have not been thoroughly
carried out. In this paper, the weight-based and MCMC-based Bayesian methods using one-step and two-step procedures are compared
to see which Bayesian method accurately and efficiently identifies a correct joint distribution through simulation studies.
It is validated that the two-step weight-based Bayesian method has the best performance. |
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