Bayesian Nonparametric Modeling for Rapid Design of Metamaterial Microstructures by Bin Liu, Chunlin Ji

“…The inference is performed using a Markov chain relying on Gibbs sampling. Experimental results demonstrate that the proposed approach is highly efficient in facilitating rapid design of MTM with accuracy.…”
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Testing Data Cloning as the Basis of an Estimator for the Stochastic Volatility in Mean Model by E. Romero, E. Ropero-Moriones

“…Notably, the estimates it provides yield superior outcomes than those derived from the Markov chain Monte Carlo (MCMC) method in terms of standard errors, all while being unaffected by the selection of prior distributions.…”
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Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID-19 Data by Rashad M. EL-Sagheer, Mohamed S. Eliwa, Khaled M. Alqahtani, Mahmoud EL-Morshedy

“…In Bayesian framework, the Bayes estimates of the unknown parameters are evaluated by applying the Markov chain Monte Carlo technique, and highest posterior density credible intervals are also carried out. …”
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Projecting the Spread of COVID-19 for Germany by Jean Roch Donsimoni, René Glawion, Bodo Plachter, Klaus Wälde

“…Their theoretical framework builds on a continuous time Markov chain with four physical states: healthy, sick, recovered or asymptomatic infected, and dead. …”
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Best Prediction Method for Progressive Type-II Censored Samples under New Pareto Model with Applications by Hanan Haj Ahmad

“…Since Bayes estimators cannot be expressed explicitly, Gibbs and the Markov Chain Monte Carlo techniques are utilized for Bayesian calculation. …”
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Finite-Time Nonfragile Dissipative Control for Discrete-Time Neural Networks with Markovian Jumps and Mixed Time-Delays by Ling Hou, Dongyan Chen, Chan He

“…This paper considers the stochastic finite-time dissipative (SFTD) control problem based on nonfragile controller for discrete-time neural networks (NNS) with Markovian jumps and mixed delays, in which the mode switching phenomenon, is described as Markov chain, and the mixed delays are composed of discrete time-varying delay and distributed delays. …”
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Self-corrected coefficient smoothing method based network security situation prediction by Hongyu YANG, Xugao ZHANG

“…In order to solve the problem of insufficient accuracy of current network security situation prediction methods,a new network security situation prediction model was proposed based on self-correcting coefficient smoothing.Firstly,a network security assessment quantification method was designed to transform the alarm information into situation real value time series based on the entropy correlation degree.Then,the adaptive solution of the static smoothing coefficient was calculated and the predicted initial value was obtained by using the variable domain space.Finally,based on the error category,the time-changing weighted Markov chain was built to modify the initial network situation prediction result and the prediction accuracy was further raised.The prediction model was tested with LL_DOS_1.0 dataset and the experimental results show that the proposed model has higher adaptability and prediction accuracy for network situation time series.…”
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A New Bayesian Network-Based Generalized Weighting Scheme for the Amalgamation of Multiple Drought Indices by Muhammad Ahmad Raza, Mohammed M. A. Almazah, Nadhir Al-ansari, Ijaz Hussain, Fuad S. Al-Duais, Mohammed A. Naser

“…These ADPPs are obtained from Bayesian networks (BNs)-based Markov Chain Monte Carlo (MCMC) simulations. Results have shown that the MWADI correlates more with the standardized precipitation index (SPI) and the standardized precipitation temperature index (SPTI). …”
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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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Exponential Stability of Stochastic Delayed Neural Networks with Inverse Hölder Activation Functions and Markovian Jump Parameters by Yingwei Li, Huaiqin Wu

“…The jumping parameters are modeled as a continuous-time finite-state Markov chain. Firstly, based on Brouwer degree properties, the existence and uniqueness of the equilibrium point for SNNs without noise perturbations are proved. …”
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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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Edge Intelligence-Based RAN Architecture for 6G Internet of Things by Yang Liu, Qingtian Wang, Haitao Liu, Jiaying Zong, Fengyi Yang

“…We also developed a Markov chain-based RAN Intelligence Control (RIC) scheduling policy for allocating intelligence elements. …”
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Modeling the Impact of Virtual Contact Network with Community Structure on the Epidemic Spreading by Jianlin Zhou, Haiyan Liu

“…We first use a microscopic Markov chain approach to characterize the coupled disease-awareness dynamics and then analyze the effect of different factors on the coevolution of information dissemination and epidemic spreading based on the Monte Carlo simulation. …”
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Mean-Square Exponential Synchronization of Markovian Switching Stochastic Complex Networks with Time-Varying Delays by Pinning Control by Jingyi Wang, Chen Xu, Jianwen Feng, Man Kam Kwong, Francis Austin

“…The switching parameters are modeled by a continuous-time, finite-state Markov chain, and the complex network is subject to noise perturbations, Markovian switching, and internal and outer time-varying delays. …”
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Modeling of Random Delays in Networked Control Systems by Yuan Ge, Qigong Chen, Ming Jiang, Yiqing Huang

“…In this paper, four major delay models are surveyed including constant delay model, mutually independent stochastic delay model, Markov chain model, and hidden Markov model. In each delay model, some promising compensation methods of delays are also addressed.…”
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Multiobjective Lightning Flash Algorithm Design and Its Convergence Analysis via Martingale Theory by Jiandong Duan, Jing Wang, Xinghua Liu, Gaoxi Xiao

“…The charge population state of the lightning flash algorithm is defined, and we prove that the charge population state sequence is a Markov chain. Since the convergence analysis of MOLFA is to investigate whether a Pareto optimal solution can be reached when the optimal charge population state is obtained, the development of a charge population state is analyzed to achieve the goal of this paper. …”
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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. …”
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A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation by Darong Huang, Zhenping Deng, Bo Mi

“…Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. …”
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Communication performance analysis of secondary users in cognitive radio networks under primary user emulation attacks by Shan-shan WANG, Xing-guo LUO, Peng LI

“…Cognitive radio network(CRN)is an effective technology and a hot research direction which could solve the problem of deficient resource and revolutionize utilization.And its safety technology attracted more and more researches.Primary user emulation(PUE)is a typically easily and largely affecting attack.PUE attacked come from both malicious misbehavior secondary users(MMU)and selfish misbehavior secondary users(SMU).The former was studied much more deeply than the later one.Distinguishing MMU and SMU,a four dimensional continuous time markov chain model to analyze the communication performance of normal secondary users under PUE attacks,and typically affected by SMUs was proposed.Furthermore,several PUE detection technologies were compared.The emulation results indicate that the SMU detection mechanism is essential for the PUE attack detection technology,which can improve the detection effects largely.…”
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Within‐chain parallelization—Giving Stan Jet Fuel for population modeling in pharmacometrics by Casey Davis, Pavan Vaddady

“…To perform a Bayesian data analysis, most models in pharmacometrics require Markov Chain Monte Carlo (MCMC) methods to sample from the posterior distribution. …”
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