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首页> 外文会议>Computers and information in engineering conference >IMPROVED MCMC METHOD FOR PARAMETER ESTIMATION BASED ON MARGINAL PROBABILITY DENSITY FUNCTION
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IMPROVED MCMC METHOD FOR PARAMETER ESTIMATION BASED ON MARGINAL PROBABILITY DENSITY FUNCTION

机译：基于边际概率密度函数的参数估计改进了MCMC方法

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摘要

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.
机译：在许多工程问题中，采样通常用于估计和量化贝叶斯框架过程中不确定参数的概率分布，这是绘制遵循参数概率特征的适当样本。在众多方法中，马尔可夫链蒙特卡罗（MCMC）由于其效率和广泛的适用性而获得了最受欢迎程度。然而，由于找到适当的提案分布，因此MCMC在增加参数和/或高相关性的情况下不起作用。本文提出了一种采用边际概率密度函数（PDF）作为提案分布的方法以克服这些问题。由贝叶斯方法制定的若干工程问题是解决了提出方法的有效性。

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