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International Journal for Uncertainty Quantification

Publication de 6  numéros par an

ISSN Imprimer: 2152-5080

ISSN En ligne: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

ASYMPTOTICALLY INDEPENDENT MARKOV SAMPLING: A NEW MARKOV CHAIN MONTE CARLO SCHEME FOR BAYESIAN INFERENCE

Volume 3, Numéro 5, 2013, pp. 445-474
DOI: 10.1615/Int.J.UncertaintyQuantification.2012004713
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RÉSUMÉ

In Bayesian inference, many problems can he expressed as the evaluation of the expectation of an uncertain quantity of interest with respect to the posterior distribution based on relevant data. Standard Monte Carlo method is often not applicable because the encountered posterior distributions cannot be sampled directly. In this case, the most popular strategies are the importance sampling method, Markov chain Monte Carlo, and annealing. In this paper, we introduce a new scheme for Bayesian inference, called asymptotically independent Markov sampling (AIMS), which is based on the above methods. We derive important ergodic properties of AIMS. In particular, it is shown that, under certain conditions, the AIMS algorithm produces a uniformly ergodic Markov chain. The choice of the free parameters of the algorithm is discussed and recommendations are provided for this choice, both theoretically and heuristically based. The efficiency of AIMS is demonstrated with three numerical examples, which include both multimodal and higher-dimensional target posterior distributions.

CITÉ PAR
  1. Zuev Konstantin M., Beck James L., Global optimization using the asymptotically independent Markov sampling method, Computers & Structures, 126, 2013. Crossref

  2. Jia Gaofeng, Taflanidis Alexandros A., Sample-based evaluation of global probabilistic sensitivity measures, Computers & Structures, 144, 2014. Crossref

  3. Duputel Zacharie, Agram Piyush S., Simons Mark, Minson Sarah E., Beck James L., Accounting for prediction uncertainty when inferring subsurface fault slip, Geophysical Journal International, 197, 1, 2014. Crossref

  4. Zuev Konstantin M., Beck James L., Asymptotically Independent Markov Sampling: A New MCMC Scheme for Bayesian Inference, Vulnerability, Uncertainty, and Risk, 2014. Crossref

  5. Green P.L., Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing, Mechanical Systems and Signal Processing, 52-53, 2015. Crossref

  6. Green P.L., Cross E.J., Worden K., Bayesian system identification of dynamical systems using highly informative training data, Mechanical Systems and Signal Processing, 56-57, 2015. Crossref

  7. Green P. L., Worden K., Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373, 2051, 2015. Crossref

  8. Noël J.P., Kerschen G., Nonlinear system identification in structural dynamics: 10 more years of progress, Mechanical Systems and Signal Processing, 83, 2017. Crossref

  9. Olivier Audrey, Smyth Andrew W., Particle filtering and marginalization for parameter identification in structural systems, Structural Control and Health Monitoring, 24, 3, 2017. Crossref

  10. Cordeiro Sergio Gustavo Ferreira, Leonel Edson Denner, Beaurepaire Pierre, Quantification of cohesive fracture parameters based on the coupling of Bayesian updating and the boundary element method, Engineering Analysis with Boundary Elements, 74, 2017. Crossref

  11. Abhinav S., Manohar C.S., Combined state and parameter identification of nonlinear structural dynamical systems based on Rao-Blackwellization and Markov chain Monte Carlo simulations, Mechanical Systems and Signal Processing, 102, 2018. Crossref

  12. Olivier Audrey, Smyth Andrew W., On the Performance of Online Parameter Estimation Algorithms in Systems with Various Identifiability Properties, Frontiers in Built Environment, 3, 2017. Crossref

  13. Lapotre M. G. A., Ehlmann B. L., Minson S. E., A probabilistic approach to remote compositional analysis of planetary surfaces, Journal of Geophysical Research: Planets, 122, 5, 2017. Crossref

  14. Vakilzadeh Majid K., Sjögren Anders, Johansson Anders T., Abrahamsson Thomas J. S., Sequential Gauss-Newton MCMC Algorithm for High-Dimensional Bayesian Model Updating, in Model Validation and Uncertainty Quantification, Volume 3, 2017. Crossref

  15. Huang Yong, Beck James L., Li Hui, Bayesian system identification based on hierarchical sparse Bayesian learning and Gibbs sampling with application to structural damage assessment, Computer Methods in Applied Mechanics and Engineering, 318, 2017. Crossref

  16. Green P. L., A MCMC Method for Bayesian System Identification from Large Data Sets, in Model Validation and Uncertainty Quantification, Volume 3, 2015. Crossref

  17. Cantero-Chinchilla Sergio, Chiachío Juan, Chiachío Manuel, Chronopoulos Dimitrios, Jones Arthur, A robust Bayesian methodology for damage localization in plate-like structures using ultrasonic guided-waves, Mechanical Systems and Signal Processing, 122, 2019. Crossref

  18. Olivier Audrey, Smyth Andrew W., A marginalized unscented Kalman filter for efficient parameter estimation with applications to finite element models, Computer Methods in Applied Mechanics and Engineering, 339, 2018. Crossref

  19. Huang Yong, Shao Changsong, Wu Biao, Beck James L., Li Hui, State-of-the-art review on Bayesian inference in structural system identification and damage assessment, Advances in Structural Engineering, 22, 6, 2019. Crossref

  20. Asadollahi Parisa, Huang Yong, Li Jian, Bayesian Finite Element Model Updating and Assessment of Cable-Stayed Bridges Using Wireless Sensor Data, Sensors, 18, 9, 2018. Crossref

  21. Green P. L., Bayesian System Identification of MDOF Nonlinear Systems Using Highly Informative Training Data, in Topics in Modal Analysis II, Volume 8, 2014. Crossref

  22. Zhang Jize, Taflanidis Alexandros A., Accelerating MCMC via Kriging-based adaptive independent proposals and delayed rejection, Computer Methods in Applied Mechanics and Engineering, 355, 2019. Crossref

  23. Bhattacharyya Pinaky, Beck James, Exploiting convexification for Bayesian optimal sensor placement by maximization of mutual information, Structural Control and Health Monitoring, 27, 10, 2020. Crossref

  24. Zhou Yue, Hu Xiaofang, Wang Lidan, Zhou Guangdong, Duan Shukai, QuantBayes: Weight Optimization for Memristive Neural Networks via Quantization-Aware Bayesian Inference, IEEE Transactions on Circuits and Systems I: Regular Papers, 68, 12, 2021. Crossref

  25. Minson S. E., Simons M., Beck J. L., Bayesian inversion for finite fault earthquake source models I—theory and algorithm, Geophysical Journal International, 194, 3, 2013. Crossref

  26. Zhao Meijie, Huang Yong, Zhou Wensong, Li Hui, Bayesian uncertainty quantification for guided-wave-based multidamage localization in plate-like structures using Gibbs sampling, Structural Health Monitoring, 20, 6, 2021. Crossref

  27. Jerez D.J., Jensen H.A., Beer M., Reliability-based design optimization of structural systems under stochastic excitation: An overview, Mechanical Systems and Signal Processing, 166, 2022. Crossref

  28. Dutta Rishabh, Jónsson Sigurjón, Vasyura‐Bathke Hannes, Simultaneous Bayesian Estimation of Non‐Planar Fault Geometry and Spatially‐Variable Slip, Journal of Geophysical Research: Solid Earth, 126, 7, 2021. Crossref

  29. Rossat D., Baroth J., Briffaut M., Dufour F., Bayesian inversion using adaptive Polynomial Chaos Kriging within Subset Simulation, Journal of Computational Physics, 455, 2022. Crossref

  30. Jerez D.J., Jensen H.A., Beer M., Chen J., Asymptotic Bayesian Optimization: A Markov sampling-based framework for design optimization, Probabilistic Engineering Mechanics, 67, 2022. Crossref

  31. Jia Gaofeng, Taflanidis Alexandros A., Beck James L., A New Adaptive Rejection Sampling Method Using Kernel Density Approximations and Its Application to Subset Simulation, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering, 3, 2, 2017. Crossref

  32. Cantero-Chinchilla Sergio, Chiachío Juan, Chiachío Manuel, Chronopoulos Dimitrios, Jones Arthur, Optimal sensor configuration for ultrasonic guided-wave inspection based on value of information, Mechanical Systems and Signal Processing, 135, 2020. Crossref

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