BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION by HE ChaoBing

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Voronoi tessellation and hierarchical model based texture image segmentation by Quan-hua ZHAO, Yu LI, Xiao-jun HE, Wei-dong SONG

“…Following Bayesian paradigm, a posterior distribution, which models the texture segmentation for a given texture image, was obtained. A metropolis-hastings algorithm was designed for simulating the posterior distribution. …”
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BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION by YE Ling, JIANG HongKang, ZOU YuQing, CHEN HuaPeng, WANG LiCheng

“…The traditional Markov Chain Monte Carlo（MCMC） simulation method is inefficient and difficult to converge in high dimensional problems and complicated posterior probability density.In order to overcome these shortcomings，a Bayesian finite element model updating algorithm based on Markov chain population competition was proposed.First，the differential evolution algorithm was introduced in the traditional method of Metropolis-Hastings algorithm.Based on the interaction of different information carried by Markov chains in the population，optimization suggestions were obtained to approach the objective function quickly.It solves the defect of sampling retention in the updating process of high-dimensional parameter model.Then，the competition algorithm was introduced，which has constant competitive incentives and a built-in mechanism for losers to learn from winners.Higher precision was obtained by using fewer Markov chains，which improves the efficiency and precision of model updating.Finally，a numerical example of finite element model updating of a truss structure was used to verify the proposed algorithm in this paper.Compared with the results of standard MH algorithm，the proposed algorithm can quickly update the high-dimensional parameter model with high accuracy and good robustness to random noise.It provides a stable and effective method for finite element model updating of large-scale structure considering uncertainty.…”
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A Fast and Efficient Markov Chain Monte Carlo Method for Market Microstructure Model by Sun Yapeng, Peng Hui, Xie Wenbiao

“…A fast and efficient Markov Chain Monte Carlo (MCMC) approach based on an efficient simulation smoother algorithm and an acceptance-rejection Metropolis–Hastings algorithm is designed to estimate the non-linear MM model. …”
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Probabilistic Power Flow Analysis of DERs Integrated Power System From a Bayesian Parameter Estimation Perspective by Wanjoli, Paul, Moustafa, Mohamed M.Zakaria, Abbasy, Nabil H.

“…By applying Bayes’ theorem, BPE estimates posterior distributions, refined by the Metropolis-Hastings algorithm. Validated on IEEE 39-bus and 59-bus test systems in MATLAB/Simulink, BPE outperformed the 2m+1 point estimate method (PEM) in terms of accuracy, computation speed and scalability. …”
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A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model by Edwiga Renald, Miracle Amadi, Heikki Haario, Joram Buza, Jean M. Tchuenche, Verdiana G. Masanja

“…The assessment of the uncertainties is determined with the help of Adaptive Metropolis Hastings algorithm, a Markov Chain Monte Carlo (MCMC) standard statistical method. …”
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Estimation for Akshaya Failure Model with Competing Risks under Progressive Censoring Scheme with Analyzing of Thymic Lymphoma of Mice Application by Tahani A. Abushal, Jitendra Kumar, Abdisalam Hassan Muse, Ahlam H. Tolba

“…We apply the Metropolis–Hasting algorithm to generate MCMC samples from the posterior density function. …”
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