Transitional Markov Chain Monte Carlo Method For Bayesian Model Updating, Model Class Selection And Model Averaging
碩士 === 國立臺灣科技大學 === 營建工程系 === 94 === This thesis presents a newly developed stochastic simulation for Bayesian model updating, model class selection and model averaging, named the transitional Markov chain Monte Carlo approach (TMCMC). The idea behind TMCMC is to avoid the problem of sampling from d...
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ndltd-TW-094NTUS55120562019-05-15T19:18:15Z http://ndltd.ncl.edu.tw/handle/4p9f4q Transitional Markov Chain Monte Carlo Method For Bayesian Model Updating, Model Class Selection And Model Averaging 漸變型馬可夫鍊蒙地卡羅法於模型更新、模型選定及模型平均之應用 Yi-Ju, Chen 陳奕竹 碩士 國立臺灣科技大學 營建工程系 94 This thesis presents a newly developed stochastic simulation for Bayesian model updating, model class selection and model averaging, named the transitional Markov chain Monte Carlo approach (TMCMC). The idea behind TMCMC is to avoid the problem of sampling from difficult posterior probability density functions (PDF) but sampling from a series of PDFs that converge to the posterior PDF and that are easier to sample. The TMCMC approach is based on Markov chain Monte Carlo (MCMC), while it is more versatile and robust than MCMC. It is shown that TMCMC is able to draw samples from some difficult PDFs, e.g. multi-modal and very peaked PDFs. The TMCMC approach can also estimate evidence of the chosen probabilistic model class conditioning on the measured data, a key component for Bayesian model class selection and model averaging. Three examples are used to demonstrate the effectiveness of the TMCMC approach in Bayesian model updating, model class selection and model averaging. Jian-Ye Ching 卿建業 2006 學位論文 ; thesis 94 zh-TW |
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碩士 === 國立臺灣科技大學 === 營建工程系 === 94 === This thesis presents a newly developed stochastic simulation for Bayesian model updating, model class selection and model averaging, named the transitional Markov chain Monte Carlo approach (TMCMC). The idea behind TMCMC is to avoid the problem of sampling from difficult posterior probability density functions (PDF) but sampling from a series of PDFs that converge to the posterior PDF and that are easier to sample. The TMCMC approach is based on Markov chain Monte Carlo (MCMC), while it is more versatile and robust than MCMC. It is shown that TMCMC is able to draw samples from some difficult PDFs, e.g. multi-modal and very peaked PDFs. The TMCMC approach can also estimate evidence of the chosen probabilistic model class conditioning on the measured data, a key component for Bayesian model class selection and model averaging. Three examples are used to demonstrate the effectiveness of the TMCMC approach in Bayesian model updating, model class selection and model averaging.
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Jian-Ye Ching |
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Jian-Ye Ching Yi-Ju, Chen 陳奕竹 |
author |
Yi-Ju, Chen 陳奕竹 |
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Yi-Ju, Chen 陳奕竹 Transitional Markov Chain Monte Carlo Method For Bayesian Model Updating, Model Class Selection And Model Averaging |
author_sort |
Yi-Ju, Chen |
title |
Transitional Markov Chain Monte Carlo Method For Bayesian Model Updating, Model Class Selection And Model Averaging |
title_short |
Transitional Markov Chain Monte Carlo Method For Bayesian Model Updating, Model Class Selection And Model Averaging |
title_full |
Transitional Markov Chain Monte Carlo Method For Bayesian Model Updating, Model Class Selection And Model Averaging |
title_fullStr |
Transitional Markov Chain Monte Carlo Method For Bayesian Model Updating, Model Class Selection And Model Averaging |
title_full_unstemmed |
Transitional Markov Chain Monte Carlo Method For Bayesian Model Updating, Model Class Selection And Model Averaging |
title_sort |
transitional markov chain monte carlo method for bayesian model updating, model class selection and model averaging |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/4p9f4q |
work_keys_str_mv |
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