Analysis and explanation sprawl in industrial cities (Case study: Bandar Mahshahr) by arash ghasemphour, azadeh arbabi, naser ebadati, fatemeh adibi, maryam rostam pishe

“…For data analysis, cross-tab, kappa coefficient, sectorial, Heldren and Markov chain models were used. In this regard, Envi 5.3, Terrset, Arc/Gis and Google Map software were used to analyze the obtained information. …”
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Modeling and Analysis of Postdisaster Aviation Medical Rescue Process Using SPN-MC by Qingsong Zhang, Xuezheng Wang, Yijia Jin, Xingna Luo

“…The dynamic discrete modeling of the postdisaster aviation medical rescue process was carried out in combination with the stochastic Petri net (SPN) theory, and the effectiveness of the process of postdisaster aviation medical rescue was analyzed by using the isomorphism relationship between SPN and Markov chain (MC). Finally, the Sichuan Jiuzhaigou Earthquake event was taken as a case study to carry out the performance analysis and sensitivity analysis of the postdisaster aviation medical rescue process model. …”
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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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Monitoring and Simulation of Dynamic Spatiotemporal Land Use/Cover Changes by Andong Guo, Yuqing Zhang, Qing Hao

“…In this study, we preprocessed multiperiod land use and socioeconomic data, combined with spatial zoning, multilayer perception (MLP) artificial neural network, and Markov chain (MC), to construct a cellular automaton model of spatial zoning. …”
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Building resilience: A holistic approach for revitalizing existing infrastructure assets by Ahmed Gouda Mohamed, Farah Alashkar, Akinsanmi Sheriff Akindeinde, Ahmed Osama Daoud

“…The developed approach incorporates asset hierarchy and classification, condition assessment via pilot survey, Markov chain model for degradation anticipation, stakeholder perception analysis through questionnaires for comparative analysis, and constructing an optimization model for fund allocation. …”
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An Analysis of the Optimal Allocation of Core Human Resources in Family Enterprises Based on the Markov Model by Jinyang Cao

“…Taking family business A as an example, the Markov chain is used to predict the flow of personnel in the company in the next three years. …”
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Measuring Spatial Expansion of Tehran Metropolis and its Impact on Environmental Changes by Asghar Teymouri, Jamileh Tavakolinia, Abolfazl Meshkini

“…The results of the Markov Chain Model prediction of the probability of land use changes and land cover up to 1405 also indicate that land use and land cover changes will continue in favor of constructed lands. …”
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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. …”
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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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A New Coupled Awareness-Epidemic Spreading Model with Neighbor Behavior on Multiplex Networks by Chao Zuo, Anjing Wang, Fenping Zhu, Zeyang Meng, Xueke Zhao

“…According to the microscopic Markov chain approach (MMCA), we analyze the dynamical evolution of the system and derive the theoretical epidemic threshold on uncorrelated heterogeneous networks, and then, we validate the analysis by numerical simulation and discuss the final size of awareness diffusion and epidemic spreading on a scale-free network. …”
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A Distance-Aware Safety-Related Message Broadcasting Algorithm for Vehicular Networks by Xiaohuan Li, Bin-jie Hu, Hongbin Chen, Jin Ye

“…Performance of the proposed algorithm is analyzed by using a 2D Markov chain. Analytical and simulation results demonstrate that the proposed algorithm can enhance the performance of safety-related message broadcasting in terms of propagation distance, which is reflected by the successful transmission probability. …”
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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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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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Optimal Asynchronous Guaranteed Cost Control of Uncertain Markov Jump Systems With Actuator Saturation and Nonideal Transition Probabilities by Jiazheng Liu, Yang Yang, Yaru Song

“…Moreover, since it is complex to obtain accurate transition probabilities for the Markov chain of the original system and the hidden Markov model in the observation process, unknown and uncertain transition probabilities are simultaneously considered in both mode transitions and mode observation processes, and a separation strategy is implemented to handle the nonideal probabilities in each process independently. …”
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