Stochastic Stability of Neural Networks with Both Markovian Jump Parameters and Continuously Distributed Delays by Quanxin Zhu, Jinde Cao

“…The jumping parameters are modeled as a continuous-time, finite-state Markov chain. By constructing appropriate Lyapunov-Krasovskii functionals, some novel stability conditions are obtained in terms of linear matrix inequalities (LMIs). …”
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Finite-State-Space Truncations for Infinite Quasi-Birth-Death Processes by Hendrik Baumann

“…For dealing numerically with the infinite-state-space Markov chains, a truncation of the state space is inevitable, that is, an approximation by a finite-state-space Markov chain has to be performed. …”
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First Hitting Place Probabilities for a Discrete Version of the Ornstein-Uhlenbeck Process by Mario Lefebvre, Jean-Luc Guilbault

“…A Markov chain with state space {0,…,N} and transition probabilities depending on the current state is studied. …”
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A Bayesian approach to discrete multiple outcome network meta-analysis. by Rebecca Graziani, Sergio Venturini

“…The remaining elements of the hierarchial random effects model are specified in a standard way, with the logit of the success probabilities given by the sum of a baseline log-odds and random effects comparing the log-odds of each treatment against the reference and having a Gaussian distribution centered at the vector of pooled effects. An adaptive Markov Chain Monte Carlo algorithm is devised for running posterior inference. …”
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An Analytical Approach to Opportunistic Transmission under Rayleigh Fading Channels by Yousaf Bin Zikria, Sung Won Kim, Heejung Yu, Seung Yeob Nam

“…Under this model, we develop a generic Markov chain model to obtain the analytical results and verify the effectiveness of the statistical analysis. …”
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Edge Statistics for Lozenge Tilings of Polygons, II: Airy Line Ensemble by Amol Aggarwal, Jiaoyang Huang

“…To realize this comparison, we require a nearly optimal concentration estimate for the tiling height function, which we establish by exhibiting a certain Markov chain on the set of all tilings that preserves such concentration estimates under its dynamics.…”
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Parameter Estimation on a Stochastic SIR Model with Media Coverage by Changguo Li, Yongzhen Pei, Meixia Zhu, Yue Deng

“…In order to reduce the computational load, the Newton-Raphson algorithm and Markov Chain Monte Carlo (MCMC) technique are incorporated with maximum likelihood estimation. …”
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Inference of Process Capability Index Cpy for 3-Burr-XII Distribution Based on Progressive Type-II Censoring by Rashad M. EL-Sagheer, Mustafa M. Hasaballah

“…The Bayesian estimates for the index Cpy have been obtained by the Markov Chain Monte Carlo method. Also, the credible intervals are constructed by using MCMC samples. …”
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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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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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