Abstract
In many engineering problems, sampling is often used to estimate and quantify the probability distribution of uncertain parameters during the course of Bayesian framework, which is to draw proper samples that follow the probabilistic feature of the parameters. Among numerous approaches, Markov Chain Monte Carlo (MCMC) has gained the most popularity due to its efficiency and wide applicability. The MCMC, however, does not work well in the case of increased parameters and/or high correlations due to the difficulty of finding proper proposal distribution. In this paper, a method employing marginal probability density function (PDF) as a proposal distribution is proposed to overcome these problems. Several engineering problems which are formulated by Bayesian approach are addressed to demonstrate the effectiveness of proposed method.
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Recommended by Associate Editor Tae Hee Lee
Dawn An received the B.S. degree and M.S. degree of mechanical engineering from Korea Aerospace University in 2008 and 2010, respectively. She is now a joint Ph.D. student at Korea Aerospace University and the University of Florida. Her current research is focused on prognostics algorithms based on the Bayesian inference.
Joo-Ho Choi received the B.S. degree of mechanical engineering from Hanyang University in 1981, the M.S. degree and Ph.D. degree of mechanical engineering from Korea Advanced Institute of Science and Technology (KAIST) in 1983 and 1987, respectively. During the year 1988, he worked as a Postdoctoral Fellow at the University of Iowa. He joined the School of Aerospace and Mechanical Engineering at Korea Aerospace University, Korea, in 1997 and is now Professor. His current research is focused on the reliability analysis, design for life-time reliability, and prognostics and health management.
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An, D., Choi, JH. Improved MCMC method for parameter estimation based on marginal probability density function. J Mech Sci Technol 27, 1771–1779 (2013). https://doi.org/10.1007/s12206-013-0428-9
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DOI: https://doi.org/10.1007/s12206-013-0428-9