Journal article
Comparison study between MCMC-based and weight-based Bayesian methods for identification of joint distribution
Structural and multidisciplinary optimization, Vol.42(6), pp.823-833
12/2010
DOI: 10.1007/s00158-010-0539-1
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.
Details
- Title: Subtitle
- Comparison study between MCMC-based and weight-based Bayesian methods for identification of joint distribution
- Creators
- Yoojeong NohK. K ChoiIkjin Lee
- Resource Type
- Journal article
- Publication Details
- Structural and multidisciplinary optimization, Vol.42(6), pp.823-833
- DOI
- 10.1007/s00158-010-0539-1
- ISSN
- 1615-147X
- eISSN
- 1615-1488
- Language
- English
- Date published
- 12/2010
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984064113802771
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