BAYESIAN FINITE ELEMENT MODEL UPDATING BASED ON MARKOV CHAIN POPULATION COMPETITION by YE Ling, JIANG HongKang, ZOU YuQing, CHEN HuaPeng, WANG LiCheng

“…The traditional Markov Chain Monte Carlo（MCMC） simulation method is inefficient and difficult to converge in high dimensional problems and complicated posterior probability density.In order to overcome these shortcomings，a Bayesian finite element model updating algorithm based on Markov chain population competition was proposed.First，the differential evolution algorithm was introduced in the traditional method of Metropolis-Hastings algorithm.Based on the interaction of different information carried by Markov chains in the population，optimization suggestions were obtained to approach the objective function quickly.It solves the defect of sampling retention in the updating process of high-dimensional parameter model.Then，the competition algorithm was introduced，which has constant competitive incentives and a built-in mechanism for losers to learn from winners.Higher precision was obtained by using fewer Markov chains，which improves the efficiency and precision of model updating.Finally，a numerical example of finite element model updating of a truss structure was used to verify the proposed algorithm in this paper.Compared with the results of standard MH algorithm，the proposed algorithm can quickly update the high-dimensional parameter model with high accuracy and good robustness to random noise.It provides a stable and effective method for finite element model updating of large-scale structure considering uncertainty.…”
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Variational quantum algorithm for low-dimensional systems in the Pauli basis by D.O. Golov, N.A. Petrov, A.N. Tsirulev

“…We propose new variational quantum algorithm based on a Monte Carlo scheme that uses a random selection of the generators for a unitary transformation, and also uses optimization of the objective functional employing the annealing or Metropolis-Hastings algorithm. The states of the quantum system in the form of a density operator and its model Hamiltonian are represented by expansions in the Pauli basis. …”
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Detecting Clinical Risk Shift Through log–logistic Hazard Change-Point Model by Shobhana Selvaraj Nadar, Vasudha Upadhyay, Savitri Joshi

“…We also carry out a simulation study and Bayesian analysis using the Metropolis–Hastings algorithm to study the properties of the proposed estimators. …”
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Tracking and climbing behavior recognition of heavy-duty trucks on roadways by Lei Tang, Jingchi Jia, Zongtao Duan, Jingyu Ma, Xin Wang, Weiwei Kong

“…We develop an extended Metropolis–Hastings algorithm to tune the parameters of the HVMove model. …”
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Unorthodox parallelization for Bayesian quantum state estimation by Hanson H Nguyen, Kody J H Law, Joseph M Lukens

“…Using a parallelized preconditioned Crank–Nicholson Metropolis–Hastings algorithm, we demonstrate our approach on simulated data and experimental results from IBM Quantum systems up to four qubits, showing significant speedups through parallelization. …”
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RPEM: Randomized Monte Carlo parametric expectation maximization algorithm by Rong Chen, Alan Schumitzky, Alona Kryshchenko, Keith Nieforth, Michael Tomashevskiy, Shuhua Hu, Romain Garreau, Julian Otalvaro, Walter Yamada, Michael N. Neely

“…Abstract Inspired from quantum Monte Carlo, by sampling discrete and continuous variables at the same time using the Metropolis–Hastings algorithm, we present a novel, fast, and accurate high performance Monte Carlo Parametric Expectation Maximization (MCPEM) algorithm. …”
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Probabilistic Power Flow Analysis of DERs Integrated Power System From a Bayesian Parameter Estimation Perspective by Wanjoli, Paul, Moustafa, Mohamed M.Zakaria, Abbasy, Nabil H.

“…By applying Bayes’ theorem, BPE estimates posterior distributions, refined by the Metropolis-Hastings algorithm. Validated on IEEE 39-bus and 59-bus test systems in MATLAB/Simulink, BPE outperformed the 2m+1 point estimate method (PEM) in terms of accuracy, computation speed and scalability. …”
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A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model by Edwiga Renald, Miracle Amadi, Heikki Haario, Joram Buza, Jean M. Tchuenche, Verdiana G. Masanja

“…The assessment of the uncertainties is determined with the help of Adaptive Metropolis Hastings algorithm, a Markov Chain Monte Carlo (MCMC) standard statistical method. …”
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The Heavy‐Tailed Inverse Power Lindley Type‐I Model: Reliability Inference and Actuarial Applications by Amal S. Hassan, Diaa S. Metwally, Mohammed Elgarhy, Rokaya Elmorsy Mohamed

“…Given the complex nature of various Bayesian estimates, the Markov Chain Monte Carlo method, which uses the Metropolis–Hastings algorithm, is employed. Monte Carlo simulation is used to evaluate the performance of the generated estimators, assessing accuracy by examining average interval length, coverage probability, and mean squared error. …”
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A New Hyperparameter Tuning Framework for Regression Tasks in Deep Neural Network: Combined-Sampling Algorithm to Search the Optimized Hyperparameters by Nguyen Huu Tiep, Hae-Yong Jeong, Kyung-Doo Kim, Nguyen Xuan Mung, Nhu-Ngoc Dao, Hoai-Nam Tran, Van-Khanh Hoang, Nguyen Ngoc Anh, Mai The Vu

“…The CASOH framework integrates the Metropolis-Hastings algorithm with a uniform random sampling approach, increasing the likelihood of identifying promising hyperparameter configurations. …”
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Modeling growth curve parameters in Peruvian llamas using a Bayesian approach by Ali William Canaza-Cayo, Rubén Herberht Mamani-Cato, Roxana Churata-Huacani, Francisco Halley Rodríguez-Huanca, Maribel Calsin-Cari, Ferdynand Marcos Huacani-Pacori, Oscar Efrain Cardenas Minaya, Júlio Sílvio de Sousa Bueno Filho

“…The MCMC method using the Metropolis-Hastings algorithm with noninformative prior distributions was applied in the Bayesian approach. …”
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Statistical inference on progressive-stress accelerated life testing for the Perk distribution under adaptive type-II hybrid censoring scheme by Eslam Hussam, Ehab M. ALMetwally

“…We use the Metropolis Hasting algorithm (Metropolis et al. in J Chem Phys 21:1087–1092, 1953) to generate samples because the posterior is not from a well-known distribution. …”
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Competing Risks Failure Model Under Progressive Censoring With Random Removals Based on the Generalized Power Half Logistic Geometric Distribution by Ahlam H. Tolba, Ahmed Ramses El-Saeed

“…The Bayesian estimate is derived using the Markov Chain Monte Carlo (MCMC) method, incorporating symmetric and asymmetric loss functions. The Metropolis-Hasting algorithm is applied to generate MCMC samples from the posterior density function. …”
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Inference with Pólya-Gamma Augmentation for US Election Law by Adam C. Hall, Joseph Kang

“…First, the method circumvents the need for analytic approximations or Metropolis–Hastings algorithms, which leads to simpler and more computationally efficient posterior inference. …”
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