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Two-stage stochastic model updating approach based on Bayesian framework and hybrid multi-population migrant genetic-metropolis-hastings algorithm for heavy-haul railway bridges

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ENGINEERING OPTIMIZATION 2025年第5期57卷 1343-1366页

作者： Yang, Hongyin Wu, Nanhao Cao, Hongyou Zhang, Wei Wuhan Inst Technol Sch Civil Engn & Architecture Wuhan Peoples R China Fujian Prov Univ Engn Res Ctr Disaster Prevent & Mitigat Southeast Putian Peoples R China Wuhan Univ Technol Sch Civil Engn & Architecture Wuhan Peoples R China

This study proposes a stochastic model updating approach using an improved multi-population migrant genetic algorithm (MMGA) with the metropolis-hastings (MH) algorithm based on the Bayesian inference approach. To enhance the search capacity of the genetic algorithm (GA), the MMGA introduces heuristic: the promotion and elimination mechanisms of urban population migration. The stochastic model updating approach includes two stages. The optimal values identified by the MMGA without considering the parameter uncertainties in the first stage are used as the initial values for the MH algorithm to accelerate Bayesian inference for uncertain parameters in the second stage. A practical application, the Yellow River Bridge with a heavy-haul railway line, demonstrates that the proposed approach offers an increased probability of gaining the global optimum, and predictions of displacements and frequencies provided by the updated finite element model are comparable to those obtained from the field tests.

关键词： Two-stage stochastic model updating Bayesian framework improved multi-population migrant genetic algorithms metropolis-hastings algorithm

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Damage Identification of Frame Structure Based on Approximate metropolis-hastings algorithm and Probability Density Evolution Method

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INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS 2022年第3N04期22卷 2240014-2240014页

作者： Yang, Y. Ling, Y. Tan, X. K. Wang, S. Wang, R. Q. Chongqing Univ MOE Key Lab New Technol Construct Cities Mt Area Chongqing 400045 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Power China Chongqing Engn Corp Ltd Chongqing 400060 Peoples R China

This paper proposes a new method to identify local damages in frame structures based on approximate metropolis-hastings (AMH) algorithm and statistical moment. By analyzing the sensitivity of different statistical moment-based damage indices, the fusion index of fourth-order displacement moment and eighth-order acceleration moment is selected. Then the local damages in frame structures are primarily evaluated by AMH algorithm, where Gibbs sampling is adopted. Finally, the uncertainty of identified local damages is analyzed by using probability density evolution method (PDEM). Numerical simulations have been conducted to compare the proposed method with other similar damage detection methods, showing that the proposed method is more time-saving due to the involvement of Gibbs sampling and more accurate in assessing the damage severity. Experimental study of a 12-story benchmark frame testing has also been carried out, further validating the effectiveness of the proposed method.

关键词： Statistical moment damage detection metropolis-hastings algorithm Gibbs sampling frame structures

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A metropolis-hastings algorithm for Task Allocation 60

A Metropolis-Hastings Algorithm for Task Allocation

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60th IEEE Conference on Decision and Control (CDC)

作者： Hamza, Doha Toonsi, Sarah Shamma, Jeff S. King Abdullah Univ Sci & Technol KAUST CEMSE Div Thuwal Saudi Arabia Univ Illinois Ind & Enterprise Syst Engn ISE Urbana IL USA

ISBN: (纸本)9781665436595

We consider a robot-location assignment problem. The problem is confounded by a location network that restricts robots' motion to neighboring locations. The problem can be optimally solved using a centralized Hungarian algorithm. We propose a distributed game-theoretic algorithm, based on the metropolis-hastings mechanism, to eliminate the need for a central coordinator. Agents are activated at random. Agents compare their action, location, against a neighboring one based on the location network. If the new location represents an improvement in agent's utility, they move with probability one, otherwise, they move with a probability proportional to the difference in the two locations' utilities. Our algorithm converges to the optimal assignment of robots to locations. We provide extensive simulations, compare with previous work, and demonstrate the versatility of the proposed algorithm to various task allocation scenarios.

关键词： Task allocation game-theoretic algorithm distributed learning metropolis-hastings algorithm

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Performance Analysis of Generalized metropolis-hastings algorithm over Mobile Wireless Sensor Networks 30

Performance Analysis of Generalized Metropolis-Hastings Algo...

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30th International Conference on Cybernetics and Informatics (K and I)

作者： Kenyeres, Martin Kenyeres, Jozef Slovak Acad Sci Inst Informat Bratislava Slovakia Sipwise GmbH Brunn Austria

ISBN: (纸本)9781728143811

In the last years, we could have witnessed significant progress in wireless sensor networks in terms of many aspects such as hardware components, communication and routing protocols, etc. Lately, the attention of both the academy and the industry has been more and more focused on the mobile variant of this technology. Even though mobility brings many advantages, a lot of challenges related to sensor networks are still not solved. In this paper, we address the generalized metropolis-hastings algorithm for distributed averaging, whose goal is to overcome negative factors affecting the sensor readings as well as suppressing data redundancy. We test the algorithm with a varied mixing parameter over stationary edge-Markovian evolving graphs in order to identify the optimal configuration of the analyzed algorithm.

关键词： Mobile wireless sensor networks metropolis-hastings algorithm data aggregation

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New Field Operational Tests Sampling Strategy Based on metropolis-hastings algorithm

New Field Operational Tests Sampling Strategy Based on Metro...

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Intelligent Systems Conference (IntelliSys)

作者： Chelbi, Nacer Eddine Gingras, Denis Sauvageau, Claude Univ Sherbrooke Lab Intelligent Vehicles LIV Sherbrooke PQ Canada PMG Technol Inc 100 Rue Landais Blainville PQ Canada

ISBN: (纸本)9783030010546;9783030010539

As recently stated by National Highway Traffic Safety Administration (NHTSA), to demonstrate the expected performance of a highly automated vehicles system, test approaches should include a combination of simulation, test track, and on-road testing. The simulation part need to be based on a probabilistic approach. To do so, an appropriate sampling strategy is often used. In this paper, we propose a new sampling strategy based on Markov Chain Monte Carlo (MCMC) methods, using metropolis-hastings algorithm to generate samples from probability distributions of Field Operational Tests (FOT);the Safety Pilot Model Deployment (SPMD) in our case. We begin by modeling the probability distribution of each test parameter retrieved from the SPMD database, two estimation methods were applied: Kernel Density Estimation method and EM algorithm. A comparison was made between the two methods to choose the best one. These distribution models are then sampled using our sampling strategy based on the metropolis-hastings algorithm.

关键词： Expectation-Maximization algorithm (EM) Field Operational Tests (FOT) Kernal Density Estimation (KDE) Kolmogorov-Smirnov test Markov Chain Monte Carlo (MCMC) metropolis-hastings algorithm Monte Carlo simulations Safety Pilot Model Deployment (SPMD)

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Applicability of Generalized metropolis-hastings algorithm in Wireless Sensor Networks 18

Applicability of Generalized Metropolis-Hastings Algorithm i...

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18th IEEE International Conference on Smart Technologies (IEEE EUROCON)

作者： Kenyeres, Martin Kenyeres, Jozef Slovak Acad Sci Inst Informat Bratislava Slovakia Sipwise GmbH Brunn Austria

ISBN: (纸本)9781538693018

The metropolis-hastings algorithm has found wide application in various areas since its definition. In this paper, the applicability of its generalized variant for distributed averaging in the wireless sensor networks is analyzed. Hence, its execution is bounded by a stopping criterion proposed for these networks to analyze which initial configuration of the implemented stopping criterion and the mixing parameter ensure the highest performance in terms of the mean square error and the convergence rate expressed as the number of the iterations necessary for the consensus achievement. The performance of the algorithm and the implemented stopping criterion is analyzed over 100 random geometric graphs and the obtained results are compared.

关键词： distributed computing metropolis-hastings algorithm wireless sensor networks stopping criterion distributed averaging

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Modified metropolis-hastings algorithm for Efficient Sampling from Discrete Probability Distributions (MMH-DPD) Applied to Field Operational Tests database (SPMD) 3

Modified Metropolis-Hastings Algorithm for Efficient Samplin...

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3rd IEEE International Conference on Intelligent Transportation Engineering (ICITE)

作者： Chelbi, Nacer Eddine Gingras, Denis Sauvageau, Claude Univ Sherbrooke LIV Sherbrooke PQ Canada PMG Technol Inc 100 Rue Landais Blainville PQ Canada

ISBN: (纸本)9781538678312

metropolis-hastings algorithm (MH) is the most popular Markov Chain Monte Carlo (MCMC) method. Essentially, the MH algorithm generates a sample, accepts or rejects the sample based on an acceptance probability that is related to the continuous target probability distribution. In this work, we propose a modified metropolis-hastings algorithm (MMH-DPD) that can draw samples from discrete probability distributions. For starters, the discrete probability distribution is replaced with a multimodal distribution and a new step after the rejection and acceptation step is added to the original algorithm. To reduce the error caused by the tail of the multimodal distribution, we used a mixture of Generalized Gaussians instead. Numerical results and a generalization of the proposed algorithm are provided. Our simulations show that the proposed sampler reliably creates a Markov chain that generates a sequence of values, in such a way that as the number of samples goes to infinity, we can guarantee that they reflect samples from the target discrete distribution.

关键词： Markov Chain Monte Carlo (MCMC) metropolis-hastings algorithm mixture of Generalized Gaussians discrete probability distribution Field Operational Tests SPMD

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An Enhanced metropolis-hastings algorithm Based on Gaussian Processes 34th

An Enhanced Metropolis-Hastings Algorithm Based on Gaussian ...

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34th IMAC Conference and Exposition on Structural Dynamics

作者： Chowdhury, Asif Terejanu, Gabriel Univ South Carolina Dept Comp Sci & Engn 315 Main St Columbia SC 29208 USA

ISBN: (纸本)9783319297545;9783319297538

Markov Chain Monte Carlo (MCMC) has become the main computational workhorse in scientific computing for solving statistical inverse problems. It is difficult however to use MCMC algorithms when the likelihood function is computational expensive to evaluate. The status quo tackles this problem by emulating the computationally models upfront based on a number of forward simulations, and then the emulator is used in the MCMC simulation instead of the actual model. However, this strategy has an obvious drawback: how should one choose the initial number of forward simulations to capture the complexity of the model response? This is the main driver question that this paper tries to answer. Here, a novel metropolis-hastings algorithm is proposed to sample from posterior distributions corresponding to computationally expensive simulations. The main innovation is emulating the likelihood function using Gaussian processes. The proposed emulator is constructed on the fly as the MCMC simulation evolves and adapted based on the uncertainty in the acceptance rate. The algorithm is tested on a number of benchmark problems where it is shown that it significantly reduces the number of forward simulations.

关键词： Gaussian process Markov chain Monte Carlo metropolis-hastings algorithm Likelihood function

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Bayesian Analysis of Mixture Normal Model via Equi-Energy Sampler and Improved metropolis-hastings algorithm 7

Bayesian Analysis of Mixture Normal Model via Equi-Energy Sa...

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6th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)

作者： Shao, Wei Zhao, Guoqing Shao, Feifei Qufu Normal Univ Sch Management Rizhao 276826 Peoples R China Shandong Univ Finance & Econ Sch Finance Jinan 250014 Peoples R China Linyi Vocat Coll Dept Engn Linyi 276017 Peoples R China

ISBN: (纸本)9781467392006

Mixture normal model provides a convenient and flexible probabilistic representation of heterogeneous data, and the estimation of parameters received considerable attention in recent years. In this paper, we propose a Bayesian analysis of mixture normal model. Because the the posterior probability density function is too complicated to be used to draw samples directly using standard Markov Chain Monte Carlo method, we use two method, the Improved metropolis-hastings algorithm and Equi-energy sampler, to conquer the drawback. We show by numerical simulations that both Equi-energy sampler and Improved metropolis-hastings algorithm outperform the standard metropolis-hastings algorithm.

关键词： Bayesian analysis Mixture model Equi-energy sampler metropolis-hastings algorithm Markov Chain Monte Carlo

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Optimal design of the Barker proposal and other locally balanced metropolis-hastings algorithms

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BIOMETRIKA 2023年第3期110卷 579-595页

作者： Vogrinc, Jure Livingstone, Samuel Zanella, Giacomo Univ Warwick Dept Stat Coventry CV4 7AL England UCL Dept Stat Sci Gower St London WC1E 6BT England Bocconi Univ Dept Decis Sci via Roentgen 1 I-20136 Milan Italy

We study the class of first-order locally balanced metropolis-hastings algorithms introduced in Livingstone & Zanella (2022). To choose a specific algorithm within the class, the user must select a balancing function g : R+ -> R+ satisfying g(t) = tg(1/t) and a noise distribution for the proposal increment. Popular choices within the class are the metropolis-adjusted Langevin algorithm and the recently introduced Barker proposal. We first establish a general limiting optimal acceptance rate of 57% and scaling of n(-1/3), as the dimension n tends to infinity among all members of the class under mild smoothness assumptions on g and when the target distribution for the algorithm is of product form. In particular, we obtain an explicit expression for the asymptotic efficiency of an arbitrary algorithm in the class, as measured by expected squared jumping distance. We then consider how to optimize this expression under various constraints. We derive an optimal choice of noise distribution for the Barker proposal, an optimal choice of balancing function under a Gaussian noise distribution, and an optimal choice of first-order locally balanced algorithm among the entire class, which turns out to depend on the specific target distribution. Numerical simulations confirm our theoretical findings, and in particular, show that a bimodal choice of noise distribution in the Barker proposal gives rise to a practical algorithm that is consistently more efficient than the original Gaussian version.

关键词： Barker proposal Locally balanced algorithm Markov chain Monte Carlo metropolis-hastings algorithm Optimal scaling

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