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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

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. …”
Get full text

Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties by Xueteng Wang, Jiandong Wang, Mengyao Wei, Yang Yue

“…Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. …”
Get full text

Unraveling the Kinematic and Morphological Evolution of the Small Magellanic Cloud by S. R. Dhanush, A. Subramaniam, S. Subramanian

“…This analysis is carried out using a robust Markov Chain Monte Carlo method, to derive up to seven kinematic parameters. …”
Get full text

An Overview of Composite Standard Elastic-Net Distribution Based on Complex Wavelet Coefficients by Tahani A. Aloafi, Hassan M. Aljohani

“…A simulated investigation is studied using the Markov Chain Monte Carlo (MCMC) tool to estimate the underlying features, where real data are involved and modelled using the proposed methods. …”
Get full text

Statistical analysis of stress–strength in a newly inverted Chen model from adaptive progressive type-Ⅱ censoring and modelling on light-emitting diodes and pump motors by Refah Alotaibi, Mazen Nassar, Zareen A. Khan, Ahmed Elshahhat

“…The Bayes estimates are obtained with the Markov Chain Monte Carlo sampling process leveraging the squared error and LINEX loss functions. …”
Get full text

Inferential Statistics from Black Hispanic Breast Cancer Survival Data by Hafiz M. R. Khan, Anshul Saxena, Elizabeth Ross, Venkataraghavan Ramamoorthy, Diana Sheehan

“…We specifically focused on Black Hispanic race. Markov Chain Monte Carlo (MCMC) method was used for obtaining the summary results of posterior parameters. …”
Get full text

Influence of Uncertainty of Soil Hydraulic Parameters on Stability of Unsaturated Slopes Based on Bayesian Updating by Hsin-Fu Yeh, Tsien-Ting Huang, Ya-Sin Yang, Chien-Chung Ke

“…Subsequently, a Markov Chain Monte Carlo (MCMC) method was used to generate random samples, and the uncertainty of the parameters was analyzed. …”
Get full text

Identifying Copy Number Variants under Selection in Geographically Structured Populations Based on -statistics by Hae-Hiang Song, Hae-Jin Hu, In-Hae Seok, Yeun-Jun Chung

“…A hierarchical Bayesian method, which is implemented via Markov Chain Monte Carlo, estimates locus-specific FST and can identify outlying CNVs loci with large values of FST. …”
Get full text

A hybrid critical channels and optimal feature subset selection framework for EEG fatigue recognition by Hanying Guo, Siying Chen, Yongjiang Zhou, Ting Xu, Yuhao Zhang, Hongliang Ding

“…To minimize redundant information, we propose an improved Reversible Jump Markov Chain Monte Carlo (RJMCMC) algorithm for selecting the optimal feature subset, ensuring both the efficiency and accuracy of fatigue recognition. …”
Get full text

A comparison of Bayesian and frequentist confidence intervals in the presence of a late Universe degeneracy by Eoin Ó. Colgáin, Saeed Pourojaghi, M. M. Sheikh-Jabbari, Darragh Sherwin

“…We explain mathematically why this non-Gaussianity arises and show, using observational Hubble data (OHD), that Markov chain Monte Carlo (MCMC) marginalisation leads to 1D posteriors that fail to track the $$\chi ^2$$ χ 2 minimum at $$68\%$$ 68 % confidence level in high redshift bins. …”
Get full text

Estimation of Urban Link Travel Time Distribution Using Markov Chains and Bayesian Approaches by Wenwen Qin, Meiping Yun

“…Finally, the current link TTD can be reconstructed by a generic Markov Chain Monte Carlo algorithm incorporating high weighted particles. …”
Get full text

Posterior Positivity Distribution Analysis of Subclinical Bluetongue in the Eastern and North-Eastern States of India: A Wakeup Call for Outbreak Preparedness by Siddhartha Narayan Joardar, Aritra Sanyal, Ahmed Abd El Wahed, Saibal Ray

“…With the aim of getting updated and refined estimates of positivity rates, the sero-surveillance data were analyzed using the Markov chain Monte Carlo (MCMC) method to calculate the positivity rates of various species across different states and agro-climatic zones. …”
Get full text

A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI by Shuo Sun, Mingchen Gu, Yingping Wang, Rongjie Lin, Lifeng Xing, Zhiyuan Xu

“…The time-varying parameters of TVP-VAR are estimated using the Markov chain Monte Carlo (MCMC) theory. Finally, the model is validated using examples from China. …”
Get full text

Uncertainty quantification of CT regularized reconstruction within the Bayesian framework by Negin Khoeiniha, Patricio Guerrero, Wim Dewulf

“…To achieve these goals, we apply a rapid regularized Markov Chain Monte Carlo (MCMC) reconstruction method [4, 5], employing the Metropolis-Adjusted Langevin Algorithm (MALA) [6] and its Lipschitz-adaptive variant (LipMALA) [7]. …”
Get full text