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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An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios by Li Chen, Hui Chen, Lu-ling Liu

“…Using the sparse prior and initial damage estimates, Markov Chain Monte Carlo (MCMC) sampling computes the posterior Probability Density Functions (PDFs) of damage parameters to determine the Maximum A Posteriori (MAP) estimates. …”
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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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Thermodynamics of modified Chaplygin-Jacobi gas and modified Chaplygin-Abel gas: Stability analysis and observational constraints by Banadipa Chakraborty, Tamal Mukhopadhyay, Debojyoti Mondal, Ujjal Debnath

“…We then perform observational analysis using CC+BAO and Pantheon+SH0ES datasets to impose constraints on our model parameters using the Markov Chain Monte Carlo (MCMC) process.…”
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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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Low-Redshift Observational Constraints on Dark Energy Cosmologies by Mohammad Malekjani

“…Applying the Markov chain Monte Carlo algorithm and using low-redshift observational data, we put cosmological constraints on dark energy cosmologies. …”
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Uncertainty Quantification of GEKO Model Coefficients on Compressible Flows by Yeong-Ki Jung, Kyoungsik Chang, Jae Hyun Bae

“…The affine invariant ensemble algorithm (AIES) is selected to characterize the posterior distribution via Markov chain Monte Carlo sampling. Calibrated model coefficients are extracted from posterior distributions obtained through Bayesian inference, which is based on the point-collocation nonintrusive polynomial chaos (NIPC) method. …”
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Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model by Ying Chen, Zhongxiang Huang

“…The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. …”
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Calibration verification for stochastic agent-based disease spread models. by Maya Horii, Aidan Gould, Zachary Yun, Jaideep Ray, Cosmin Safta, Tarek Zohdi

“…The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). …”
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Advanced Monte Carlo for Acquisition Sampling in Bayesian Optimization by Javier Garcia-Barcos, Ruben Martinez-Cantin

“…We introduce a simplified version of Boltzmann sampling, and we analyze multiple Markov chain Monte Carlo (MCMC) methods with a numerically improved log EI implementation for acquisition sampling. …”
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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

“…The Bayes estimate is obtained by using the Markov Chain Monte Carlo (MCMC) method under symmetric and asymmetric loss functions. …”
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Reliability analysis of new jointly Type-II hybrid NH censored data and its modeling for three engineering cases by Maysaa Elmahi Abd Elwahab, Ahmed Elshahhat, Ohud A. Alqasem, Mazen Nassar

“…The Bayesian estimates are obtained using the squared error loss function and the Markov Chain Monte Carlo procedure. To assess the performance of these different estimation methods, we conduct a simulation study that incorporates various testing plans. …”
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Determining Parameters of Metal-Halide Perovskites Using Photoluminescence with Bayesian Inference by Manuel Kober-Czerny, Akash Dasgupta, Seongrok Seo, Florine M. Rombach, David P. McMeekin, Heon Jin, Henry J. Snaith

“…In this work, we demonstrate that time-resolved photoluminescence data of metal halide perovskites can be effectively evaluated by combining Bayesian inference with a Markov-chain Monte-Carlo algorithm and a physical model. This approach enables us to infer a high number of parameters that govern the performance of metal halide perovskite-based devices, alongside the probability distributions of those parameters, as well as correlations among all parameters. …”
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Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania by Erick A. Kyojo, Silas S. Mirau, Sarah E. Osima, Verdiana G. Masanja

“…Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. …”
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Analysis of competing risks model using the generalized progressive hybrid censored data from the generalized Lomax distribution by Amal Hassan, Sudhansu Maiti, Rana Mousa, Najwan Alsadat, Mahmoued Abu-Moussa

“…Bayesian estimators under gamma priors with different loss functions were generated using Markov chain Monte Carlo, and confidence intervals (CIs) were generated using the ML estimation method. …”
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