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. …”
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Bayesian Inference and Global Sensitivity Analysis for Ambient Solar Wind Prediction by Opal Issan, Pete Riley, Enrico Camporeale, Boris Kramer

“…The UQ framework utilizes variance‐based global sensitivity analysis followed by Bayesian inference via Markov chain Monte Carlo to learn the posterior densities of the most influential parameters. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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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. …”
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Probing Cosmic Isotropy with the FAST All Sky H i Survey by Yi-Wen Wu, Jun-Qing Xia

“…We apply the Markov Chain Monte Carlo method to fit these 2PACFs with a power-law model and assess the statistical significance of the best-fit parameters for the 10 FASHI sky regions by comparing them against results from mock catalogs generated under the assumptions of homogeneity and isotropy. …”
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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. …”
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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]. …”
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Bayesian Approach to Equipartition Estimation of Magnetic Field Strength by Adam A. Zychowicz, Krzysztof T. Chyży

“…In the examples presented, we used two different Markov Chain Monte Carlo methods to generate the posterior distribution of the magnetic field. …”
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Cosmic Chronometers, Pantheon+ Supernovae, and Quasars Favor Coasting Cosmologies over the Flat ΛCDM Model by Peter Raffai, Adrienn Pataki, Rebeka L. Böttger, Alexandra Karsai, Gergely Dálya

“…We used the emcee code for fitting CC data, a custom Markov Chain Monte Carlo implementation for SNe and QSOs, and Anderson–Darling tests for normality on normalized residuals for model comparison. …”
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A novel framework for uncertainty quantification of rainfall–runoff models based on a Bayesian approach focused on transboundary river basins by Thi-Duyen Nguyen, Duc Hai Nguyen, Hyun-Han Kwon, Deg-Hyo Bae

“…By utilizing an adaptive Markov chain Monte Carlo (MCMC) simulation method combined with three comprehensive uncertainty assessment measures, the developed framework focuses on evaluating the uncertainty inherent in RRMs. …”
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Comparison of three recent discrete stochastic inversion methods and influence of the prior choice by Juda, Przemysław, Straubhaar, Julien, Renard, Philippe

“…In this work, we present and compare three recent inverse frameworks: Posterior Population Expansion (PoPEx), Ensemble Smoother with Multiple Data Assimilation (ESMDA), and DREAM-ZS (a Markov chain Monte Carlo sampler). PoPEx and ESDMA are used with Multiple-point statistics (MPS) as geostatistical engines, and DREAM-ZS is used with a Wasserstein generative adversarial network (WGAN). …”
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Phylogenetic Analysis of Dengue Virus in Bangkalan, Madura Island, East Java Province, Indonesia by Teguh Hari Sucipto, Tomohiro Kotaki, Kris Cahyo Mulyatno, Siti Churrotin, Amaliah Labiqah, Soegeng Soegijanto, Masanori Kameoka

“…Serotyping was conducted using a multiplex Reverse Transcriptase-Polymerase Chain Reaction and a phylogenetic analysis of E gene sequences was performed using the Bayesian Markov chain Monte Carlo (MCMC) method. 17 out of 359 blood samples (4.7%) were positive for the isolation of DENV. …”
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