Modeling Repayment Behavior of Consumer Loan in Portfolio across Business Cycle: A Triplet Markov Model Approach by Shou Chen, Xiangqian Jiang

“…The corresponding Markov chain Monte Carlo algorithms of the particular TMM are also developed for estimating the model parameters. …”
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Bayesian Non-Parametric Mixtures of GARCH(1,1) Models by John W. Lau, Ed Cripps

“…Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described. …”
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Statistical Investigation of Bearing Capacity of Pile Foundation Based on Bayesian Reliability Theory by Zuolong Luo, Fenghui Dong

“…In order to improve the estimation accuracy of bearing capacity of pile foundation, a new forecast method of bearing capacity of pile foundation was proposed on Jeffrey’s noninformative prior using the MCMC (Markov chain Monte Carlo) method of the Bayesian theory. The proposed approach was used to estimate the parameters of Normal distribution. …”
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Estimations in a Constant-Stress Partially Accelerated Life Test for Generalized Rayleigh Distribution under Type-II Hybrid Censoring Scheme by Abdalla Rabie, Eslam Hussam, Abdisalam Hassan Muse, Ramy Abdelhamid Aldallal, Amirah Saeed Alharthi, Hassan M. Aljohani

“…Bayesian and E-Bayesian estimates are obtained using Markov chain Monte Carlo (MCMC) methods. To prove the applicability and the importance of the subject, a test for real data will be provided. …”
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An Incremental-Hybrid-Yager’s Entropy Model for Dynamic Portfolio Selection with Fuzzy Variable by Yin Li, Jian Tao, Yazhi Song

“…At last, a compromised genetic algorithm is designed, and the numerical example shows that the proposed model achieves solid returns compared against the mean-variance model and Markov chain Monte Carlo method.…”
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Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models by Nachatchapong Kaewsompong, Paravee Maneejuk, Woraphon Yamaka

“…As there are many parameters to be estimated, we consider the Bayesian Markov chain Monte Carlo approach to estimate the parameter interests in the model. …”
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LED Lighting System Reliability Modeling and Inference via Random Effects Gamma Process and Copula Function by Huibing Hao, Chun Su, Chunping Li

“…Considering the model is so complicated and analytically intractable, the Markov chain Monte Carlo (MCMC) method is used to estimate the unknown parameters. …”
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Using the Bayesian Model Averaging Approach for Genomic Selection by Considering Skewed Error Distributions by Azadeh Ghazanfari, Afshin Fayyaz Movaghar

“…Occam’s window and Markov-Chain Monte Carlo model composition (MC3) were used to determine the best model and its uncertainty. …”
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Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System by Yong-Sheng Zhang, En-Jian Yao

“…A new estimation model based on Bayesian inference formulation is proposed in this paper by integrating the probability measurement of the OD pair with only one effective route, in which all kinds of times follow the truncated normal distributions. Then, Markov Chain Monte Carlo method is designed to estimate all parameters endogenously. …”
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A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk by Lewei Duan, Duncan C. Thomas

“…The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. …”
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Estimation of the coefficients of variation for inverse power Lomax distribution by Samah M. Ahmed, Abdelfattah Mustafa

“…Additionally, it is recommended to use the Markov Chain Monte Carlo (MCMC) method to calculate the Bayes estimate and generate posterior distributions. …”
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Modified Chen distribution: Properties, estimation, and applications in reliability analysis by M. G. M. Ghazal

“…Bayesian estimates of the model parameters, along with the survival and hazard functions and their corresponding credible intervals, were derived via the Markov chain Monte Carlo method under balanced squared error loss, balanced linear-exponential loss, and balanced general entropy loss. …”
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On the Effect of Estimation Error for the Risk-Adjusted Charts by Sajid Ali, Naila Altaf, Ismail Shah, Lichen Wang, Syed Muhammad Muslim Raza

“…To compute the average run length (ARL), Markov Chain Monte Carlo simulations are conducted. Furthermore, a bootstrap method is also used to compute the ARL assuming different Phase-I data sets to minimize the effect of estimation error on risk-adjusted control charts. …”
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Nonlinear functional response parameter estimation in a stochastic predator-prey model by Gianni Gilioli, Sara Pasquali, Fabrizio Ruggeri

“…We tackle the problem of parameter estimation using a Bayesian approach relying on a Markov Chain Monte Carlo algorithm. The efficiency of the method is tested on a set of simulated data. …”
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The Partial Power Control Algorithm of Underwater Acoustic Sensor Networks Based on Outage Probability Minimization by Yun Li, Yishan Su, Zhigang Jin, Sumit Chakravarty

“…The proposed algorithm captures transmission loss (TL) using the Markov chain Monte Carlo (MCMC) method and estimates CSI in the next moment using AR prediction. …”
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Bayesian Probabilistic Framework for Damage Identification of Steel Truss Bridges under Joint Uncertainties by Wei Zheng, Yi Yu

“…A new sampling method of the transitional Markov chain Monte Carlo is incorporated with the structure’s finite element model for implementing the approach to damage identification of truss structures. …”
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Bayesian Estimation of Ammunition Demand Based on Multinomial Distribution by Kang Li, Xian-ming Shi, Juan Li, Mei Zhao, Chunhua Zeng

“…Bayesian inference is made through the Markov chain Monte Carlo method based on Gibbs sampling, and ammunition demand at different damage grades is obtained by referring to cumulative damage probability. …”
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Demonstration of Emulator-Based Bayesian Calibration of Safety Analysis Codes: Theory and Formulation by Joseph P. Yurko, Jacopo Buongiorno, Robert Youngblood

“…Use of a fast emulator makes the calibration processes used here with Markov Chain Monte Carlo (MCMC) sampling feasible. This work uses Gaussian Process (GP) based emulators, which have been used previously to emulate computer codes in the nuclear field. …”
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Damage Evaluation of Bridge Hanger Based on Bayesian Inference: Analytical Model by Yang Ding, Jing-liang Dong, Tong-lin Yang, Zhong-ping Wang, Shuang-xi Zhou, Yong-qi Wei, An-ming She

“…In order to solve the complex analytical expressions in damage evaluation model, the Metropolis-Hastings (MH) sampling of Markov chain Monte Carlo (MCMC) method was used. Three case studies are adopted to demonstrate the effect of the initial value and the applicability of the proposed model. …”
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Bayesian inference for the parameters of the generalized logistic distribution under a combined framework of generalized type-I and type-II hybrid censoring schemes with applicatio... by Mustafa M. Hasaballah, Oluwafemi Samson Balogun, M. E. Bakr

“…Key objectives include the development of maximum likelihood estimators and asymptotic confidence intervals, alongside Bayesian estimation techniques using Markov chain Monte Carlo methods. These advancements facilitate the computation of credible intervals under various loss functions, thereby improving estimation efficiency. …”
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