Optimal Maneuver Strategy of Observer for Bearing-Only Tracking in Threat Environment by Renke He, Shuxin Chen, Hao Wu, Zhuowei Liu, Jianhua Chen

“…The quantization method was used to discretize the BOT process and calculate the transition matrix of Markov chain; to achieve quantization in the beginning of each period, CKF was applied to provide the initial state estimate and the corresponding error covariance. …”
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Splitting Travel Time Based on AFC Data: Estimating Walking, Waiting, Transfer, and In-Vehicle Travel Times in Metro System by Yong-Sheng Zhang, En-Jian Yao

“…A new estimation model based on Bayesian inference formulation is proposed in this paper by integrating the probability measurement of the OD pair with only one effective route, in which all kinds of times follow the truncated normal distributions. Then, Markov Chain Monte Carlo method is designed to estimate all parameters endogenously. …”
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BeiDou satellites cross-regional communication path assignment model and resource management by Sheng Liu, Di Wu, Lanyong Zhang

“…Therefore, in this study, we develop a path assignment model based on the idea of clustering and Markov chain. The optimal path is determined by the objective function based on the maximum transition probability. …”
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Computational Procedures for a Class of GI/D/k Systems in Discrete Time by Md. Mostafizur Rahman, Attahiru Sule Alfa

“…Then the queue length is set up as a quasi-birth-death (QBD) type Markov chain. It is shown that this transformed GI/D/1 system has special structures which make the computation of the matrix R simple and efficient, thereby reducing the number of multiplications in each iteration significantly. …”
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Prophet: A Context-Aware Location Privacy-Preserving Scheme in Location Sharing Service by Jiaxing Qu, Guoyin Zhang, Zhou Fang

“…First, we define fingerprint identification based on Markov chain and state classification to describe the users’ behavior patterns. …”
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A Bayesian Hierarchical Model for Relating Multiple SNPs within Multiple Genes to Disease Risk by Lewei Duan, Duncan C. Thomas

“…The entire model is fitted using Markov chain Monte Carlo methods. Simulation studies show that the approach is capable of recovering many of the truly causal SNPs and genes, depending upon their frequency and size of their effects. …”
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Bayesian Estimation and Prediction for Flexible Weibull Model under Type-II Censoring Scheme by Sanjay Kumar Singh, Umesh Singh, Vikas Kumar Sharma

“…Since the predictive posteriors are not in the closed form, we proposed to use the Monte Carlo Markov chain (MCMC) methods to approximate the posteriors of interest. …”
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Estimation of the coefficients of variation for inverse power Lomax distribution by Samah M. Ahmed, Abdelfattah Mustafa

“…Additionally, it is recommended to use the Markov Chain Monte Carlo (MCMC) method to calculate the Bayes estimate and generate posterior distributions. …”
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Performance Analysis for Priority-Based Broadcast in Vehicular Networks by Rinara Woo, Jung-Hoon Song, Dong Seog Han

“…Firstly, an analytical Markov chain model for vehicle-to-vehicle (V2V) ad hoc communication networks is proposed for broadcasting messages with priority based on the IEEE 802.11p wireless access for vehicular environments (WAVE) standard. …”
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A Mixed Prediction Model of Ground Subsidence for Civil Infrastructures on Soft Ground by Kiyoshi Kobayashi, Kiyoyuki Kaito

“…Concretely speaking, in order to estimate the updating models, Markov Chain Monte Calro method, which is the frontier technique in Bayesian statistics, is applied. …”
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Markov Switching Model Analysis of Implied Volatility for Market Indexes with Applications to S&P 500 and DAX by Luca Di Persio, Samuele Vettori

“…In particular the volatility parameter is treated as an unobserved state variable whose value in time is given as the outcome of an unobserved, discrete-time and discrete-state, stochastic process represented by a suitable Markov chain. We will take into account two different approaches for inference on Markov switching models, namely, the classical approach based on the maximum likelihood techniques and the Bayesian inference method realized through a Gibbs sampling procedure. …”
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Modeling Campaign Optimization Strategies in Political Elections under Uncertainty by Christopher, Senfuka, Paul, Kizito Mubiru, Maureen, N. Ssempijja

“…In most political campaigns,the overall goal of every candidate is to maximize the number of voters during the election exercise.In such an effort,cost effective methods in choosing the optimal campaign strategy areparamount.In this paper, a mathematical model is proposed that optimize campaign strategies of a political candidate.Considering uncertainty in voter support and cost implications in holding political rallies,we formulate a finite state markov decision process model where states of a markov chain represent possible states of support among voters.Using daily equal intervals,thecandidates‟s decision of whether or not to campaign and hold a political rally at a given location were made using discrete time Markov chains and dynamic programming over a finite period planning horizon.Empirical data was collected from two locations on a daily basis during the campaign exercise.The data collected was analyzed and tested to establish the optimal campaign strategy and costs at the respective locations.Results from the study indicated the existence of an optimal state-dependent campaign strategy and costs at the respective political rally locations.…”
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Modified Chen distribution: Properties, estimation, and applications in reliability analysis by M. G. M. Ghazal

“…Bayesian estimates of the model parameters, along with the survival and hazard functions and their corresponding credible intervals, were derived via the Markov chain Monte Carlo method under balanced squared error loss, balanced linear-exponential loss, and balanced general entropy loss. …”
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On the Effect of Estimation Error for the Risk-Adjusted Charts by Sajid Ali, Naila Altaf, Ismail Shah, Lichen Wang, Syed Muhammad Muslim Raza

“…To compute the average run length (ARL), Markov Chain Monte Carlo simulations are conducted. Furthermore, a bootstrap method is also used to compute the ARL assuming different Phase-I data sets to minimize the effect of estimation error on risk-adjusted control charts. …”
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A two-layer network model of the evolution of public risk perception of emerging technologies by Xiaqun Liu, Xiaoyue Qiu, Yaming Zhuang

“…The evolutionary threshold of public risk perception is analysed using the microscopic Markov chain approach. The influence of public composition and the spread of risk events on the evolution of risk perception is further verified through a simulation. …”
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Efficient Method to Approximately Solve Retrial Systems with Impatience by Jose Manuel Gimenez-Guzman, M. Jose Domenech-Benlloch, Vicent Pla, Jorge Martinez-Bauset, Vicente Casares-Giner

“…This novel technique does not rely on the numerical solution of the steady-state Kolmogorov equations of the Continuous Time Markov Chain as it is common for this kind of systems but it considers the system in its Markov Decision Process setting. …”
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Dynamical Models of Tuberculosis and Their Applications by Carlos Castillo-Chavez, Baojun Song

“…Modelformulations involve a variety of mathematical areas, such as ODEs(Ordinary Differential Equations) (both autonomous andnon-autonomous systems), PDEs (Partial Differential Equations),system of difference equations, system of integro-differentialequations, Markov chain model, and simulation models.…”
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Stochastic process-based drought monitoring and assessment system: A temporal switched weights approach for accurate and precise drought determination. by Muhammad Asif Khan, Sergey Barykin, Dmitry Karpov, Nikita Lukashevich, Akram Ochilov, Rizwan Munir

“…As a result, a novel system for meteorological, agricultural, and hydrological droughts based on the Stochastic Process (Markov chain (MC)) has been proposed to address this issue. …”
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Exhaust Emission Assessment with Energy Structural Evolution in Transportation Network by Hongzhi Lin

“…However, the dynamic environmental impact assessment along with energy structural evolution in transportation network is still wondering as the vehicular exhaust emissions are highly dependent on their market shares and working conditions. In this paper, a Markov chain model is formulated to represent the transition process between traditional internal combustion engine vehicles (ICEVs), plug-in electric vehicles (PEVs), and hybrid electric vehicles (HEVs), with which the dynamic market penetration level of three vehicle types can be predicted. …”
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Network-level reproduction number and extinction threshold for vector-borne diseases by Ling Xue, Caterina Scoglio

“…Relationships between basic reproduction numbers of two deterministic network-based ordinary differential equation vector-host models, and extinction thresholds of corresponding stochastic continuous-time Markov chain models are derived under some assumptions. …”
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