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Sampling methods for solving Bayesian model updating problems: A tutorial

Lye, Adolphus, Cicirello, Alice, Patelli, Edoardo·2021·Mechanical Systems and Signal Processing
DOI 10.1016/j.ymssp.2021.107760
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  1. Waisman, 2009, Detection and quantification of flaws in structures by the extended finite element method and genetic algorithms, International Journal for Numerical Methods in Engineering, 82, 303, 10.1002/nme.2766 · DOI
  2. Hlaváěek, 2004, Reality, Mathematics, and Computation, North-Holland Series in Applied Mathematics and Mechanics Uncertain Input Data Problems and the Worst Scenario Method, 1
  3. Helton, 2004, An exploration of alternative approaches to the representation of uncertainty in model predictions, Reliability Engineering and System Safety, 85, 39, 10.1016/j.ress.2004.03.025 · DOI
  4. Helton, 1997, Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty, Journal of Statistical Computation and Simulation, 57, 3, 10.1080/00949659708811803 · DOI
  5. Winkler, 1996, Uncertainty in probabilistic risk assessment, Reliability Engineering and System Safety, 54, 127, 10.1016/S0951-8320(96)00070-1 · DOI
  6. Meng, 2020, New hybrid reliability-based topology optimization method combining fuzzy and probabilistic models for handling epistemic and aleatory uncertainties, Computer Methods in Applied Mechanics and Engineering, 363, 10.1016/j.cma.2020.112886 · DOI
  7. Luo, 2011, Reliability-based design optimization of adhesive bonded steel–concrete composite beams with probabilistic and non-probabilistic uncertainties, Engineering Structures, 33, 2110, 10.1016/j.engstruct.2011.02.040 · DOI
  8. Ni, 2016, An improved multidimensional parallelepiped non-probabilistic model for structural uncertainty analysis, Applied Mathematical Modelling, 40, 4727, 10.1016/j.apm.2015.11.047 · DOI
  9. Ni, 2018, Discussions on non-probabilistic convex modelling for uncertain problems, Applied Mathematical Modelling, 59, 54, 10.1016/j.apm.2018.01.026 · DOI
  10. Rashki, 2018, Hybrid control variates-based simulation method for structural reliability analysis of some problems with low failure probability, Applied Mathematical Modelling, 60, 220, 10.1016/j.apm.2018.03.009 · DOI
  11. Khodaparast, 2011, Interval model updating with irreducible uncertainty using the Kriging predictor, Mechanical Systems and Signal Processing, 25, 1204, 10.1016/j.ymssp.2010.10.009 · DOI
  12. Beck, 1998, Updating models and their uncertainties. I: Bayesian statistical framework, Journal of Engineering Mechanics, 124, 455, 10.1061/(ASCE)0733-9399(1998)124:4(455) · DOI
  13. Katafygiotis, 1998, Updating models and their uncertainties. II: Model identifiability, Journal of Engineering Mechanics, 124, 463, 10.1061/(ASCE)0733-9399(1998)124:4(463) · DOI
  14. Katafygiotis, 1998, A probabilistic approach to structural model updating, Soil Dynamics and Earthquake Engineering, 17, 495, 10.1016/S0267-7261(98)00008-6 · DOI
  15. Lam, 2004, Application of a statistical model updating approach on phase I of the IASC-ASCE structural health monitoring benchmark study, Journal of Engineering Mechanics, 130, 34, 10.1061/(ASCE)0733-9399(2004)130:1(34) · DOI
  16. Au, 2012, Connecting Bayesian and frequentist quantification of parameter uncertainty in system identification, Mechanical Systems and Signal Processing, 29, 328, 10.1016/j.ymssp.2012.01.010 · DOI
  17. Giagopoulos, 2019, Structural health monitoring and fatigue damage estimation using vibration measurements and finite element model updating, Structural Health Monitoring, 18, 1189, 10.1177/1475921718790188 · DOI
  18. Ching, 2004, New Bayesian model updating algorithm applied to a structural health monitoring benchmark, Structural Health Monitoring: An International Journal, 3, 313, 10.1177/1475921704047499 · DOI
  19. Yuen, 2006, Efficient model updating and health monitoring methodology using incomplete modal data without mode matching, Structural Control Health Monitoring, 13, 91, 10.1002/stc.144 · DOI · Paper page
  20. S.E. Azam, C. Papadimitriou, E. Chatzi, Recursive Bayesian filtering for displacement estimation via output-only vibration measurements, Proceedings of the 2014 World Congress on Advances in Civil, Environmental, and Materials Research (2014).
  21. J.P. Noel, M. Schoukens, Cross-fertilising research in nonlinear system identification between the mechanical, control and machine learning fields: Editorial statement, Mechanical Systems and Signal Processing 130 (213–220) 2019. doi:10.1016/j.ymssp.2019.04.061. · DOI
  22. Capellari, 2016, Optimal sensor placement through bayesian experimental design: Effect of measurement noise and number of sensors, Proceedings, 1, 41
  23. Daneshmand, 2013, A 3D hybrid BE–FE solution to the forward problem of electrical impedance tomography, Engineering Analysis with Boundary Elements, 37, 757, 10.1016/j.enganabound.2013.01.016 · DOI
  24. Xu, 2010, Analytical solution for the forward problem of magnetic induction tomography with multi-layer sphere model, Lecture Notes in Computer Science Life System Modeling and Intelligent Computing, 42
  25. Xu, 2010, Galerkin boundary element method for the forward problem of ERT, Flow Measurement and Instrumentation, 21, 172, 10.1016/j.flowmeasinst.2009.12.004 · DOI
  26. Shi, 2017, Variance reduction for global response problem based on forward Monte Carlo calculation, Nuclear Engineering and Design, 322, 291, 10.1016/j.nucengdes.2017.07.006 · DOI
  27. Elidrysy, 2020, Simulation of an extended 3D mixed Ising model by Monte Carlo method, Materials Today: Proceedings, 30, 993
  28. Li, 2019, Surrogate Model Monte Carlo simulation for stochastic flutter analysis of wind turbine blades, Journal of Wind Engineering and Industrial Aerodynamics, 188, 43, 10.1016/j.jweia.2019.02.004 · DOI
  29. Albert, 1993, Teaching Bayesian statistics using sampling methods and MINITAB, The American Statistician, 47, 182, 10.1080/00031305.1993.10475973 · DOI
  30. Papadopoulos, 2001, Uncertainty estimation and monte carlo simulation method, Flow Measurement and Instrumentation, 12, 291, 10.1016/S0955-5986(01)00015-2 · DOI
  31. Simoen, 2015, Dealing with uncertainty in model updating for damage assessment: A review, Mechanical Systems and Signal Processing, 56–57, 123, 10.1016/j.ymssp.2014.11.001 · DOI · Paper page
  32. Rossi, 2018, 10.1002/9781118771075 · DOI
  33. Karimi, 2014, A maximum-likelihood method for estimating parameters, stochastic disturbance intensities and measurement noise variances in nonlinear dynamic models with process disturbances, Computers and Chemical Engineering, 67, 178, 10.1016/j.compchemeng.2014.04.007 · DOI
  34. Szabo, 2020, Semiparametric sieve maximum likelihood estimation for accelerated hazards model with interval-censored data, Journal of Statistical Planning and Inference, 205, 175, 10.1016/j.jspi.2019.07.002 · DOI
  35. Li, 2020, Random noise suppression and parameter estimation for Magnetic Resonance Sounding signal based on maximum likelihood estimation, Journal of Applied Geophysics, 176, 10.1016/j.jappgeo.2020.104007 · DOI
  36. T. Bayes, Price, LII. An Essay towards Solving a problem in the Doctrine of Chances. By the late Rev. Mr. Bayes, F. R. S. communicated by Mr. Price, in a letter to John Canton, A. M. F. R. S., Philosophical Transactions of the Royal Society of London 53 (1763) 370–418. doi:10.1098/rstl.1763.0053. · DOI
  37. Faes, 2019, A multivariate interval approach for inverse uncertainty quantification with limited experimental data, Mechanical Systems and Signal Processing, 118, 534, 10.1016/j.ymssp.2018.08.050 · DOI
  38. Jaynes, 1988, The relation of bayesian and maximum entropy methods, Maximum-Entropy and Bayesian Methods in Science and Engineering, 25, 10.1007/978-94-009-3049-0_2 · DOI
  39. Teloli, 2021, Bayesian model identification of higher-order frequency response functions for structures assembled by bolted joints, Mechanical Systems and Signal Processing, 151, 10.1016/j.ymssp.2020.107333 · DOI
  40. Yan, 2020, A fast bayesian inference scheme for identification of local structural properties of layered composites based on wave and finite element-assisted metamodeling strategy and ultrasound measurements, Mechanical Systems and Signal Processing, 143, 10.1016/j.ymssp.2020.106802 · DOI
  41. He, 2017, Guided wave-based identification of multiple cracks in beams using a bayesian approach, Mechanical Systems and Signal Processing, 84, 324, 10.1016/j.ymssp.2016.07.013 · DOI
  42. Patelli, 2017, Model updating strategy of the DLR-AIRMOD test structure, Procedia Engineering, 199, 978, 10.1016/j.proeng.2017.09.221 · DOI
  43. Ritto, 2016, Uncertain boundary condition bayesian identification from experimental data: a case study on a cantilever beam, Mechanical Systems and Signal Processing, 68–69, 176, 10.1016/j.ymssp.2015.08.010 · DOI
  44. Green, 2018, Estimating the parameters of dynamical systems from big data using sequential Monte Carlo samplers, Mechanical Systems and Signal Processing, 93, 379, 10.1016/j.ymssp.2016.12.023 · DOI
  45. Jalali, 2019, Stochastic modelling and updating of a joint contact interface, Mechanical Systems and Signal Processing, 129, 645, 10.1016/j.ymssp.2019.04.003 · DOI
  46. Rogers, 2020, On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification, Mechanical Systems and Signal Processing, 140, 10.1016/j.ymssp.2019.106580 · DOI
  47. Jennings, 2017, astroABC: An Approximate Bayesian Computation Sequential Monte Carlo sampler for cosmological parameter estimation, Astronomy and Computing, 19, 16, 10.1016/j.ascom.2017.01.001 · DOI
  48. Wang, 2020, Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model, Mechanical Systems and Signal Processing, 139, 10.1016/j.ymssp.2020.106654 · DOI
  49. Ching, 2007, Transitional Markov Chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging, Journal of Engineering Mechanics, 133, 10.1061/(ASCE)0733-9399(2007)133:7(816) · DOI
  50. Rappel, 2019, Identifying elastoplastic parameters with Bayes’ theorem considering output error, input error and model uncertainty, Probabilistic Engineering Mechanics, 55, 28, 10.1016/j.probengmech.2018.08.004 · DOI
  51. Goller, 2011, Investigation of model uncertainties in Bayesian structural model updating, Journal of Sound and Vibration, 330, 6122, 10.1016/j.jsv.2011.07.036 · DOI
  52. Simoen, 2013, On prediction error correlation in Bayesian model updating, Journal of Sound and Vibration, 332, 4136, 10.1016/j.jsv.2013.03.019 · DOI · Paper page
  53. Han, 2017, A Bayesian inference framework for predicting the long-term deflection of concrete structures caused by creep and shrinkage, Engineering Structures, 142, 46, 10.1016/j.engstruct.2017.03.055 · DOI
  54. Bazant, 1984, Bayesian statistical prediction of concrete creep and shrinkage, ACI Journal Proceedings, 81, 319
  55. Keitel, 2012, Selecting creep models using Bayesian methods, Materials and Structures, 45, 1513, 10.1617/s11527-012-9854-x · DOI
  56. Guan, 2011, Model selection, updating, and averaging for probabilistic fatigue damage prognosis, Structural Safety, 33, 242, 10.1016/j.strusafe.2011.03.006 · DOI
  57. Huang, 2012, A probabilistic damage detection approach using vibration-based nondestructive testing, Structural Safety, 38, 11, 10.1016/j.strusafe.2012.01.004 · DOI
  58. Yan, 2015, A novel Bayesian approach for structural model updating utilizing statistical modal information from multiple setups, Structural Safety, 52, 260, 10.1016/j.strusafe.2014.06.004 · DOI
  59. Peralta, 2020, Bayesian identification of electromechanical properties in piezoelectric energy harvesters, Mechanical Systems and Signal Processing, 141, 10.1016/j.ymssp.2019.106506 · DOI
  60. Lerimonti, 2020, An innovative continuous bayesian model updating method for base-isolated RC buildings using vibration monitoring data, Mechanical Systems and Signal Processing, 139
  61. Cheung, 2017, A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal data, Mechanical Systems and Signal Processing, 92, 156, 10.1016/j.ymssp.2017.01.015 · DOI
  62. Yin, 2021, Selection of masters in dynamic reduction-based structural health monitoring using Bayesian experimental design, Mechanical Systems and Signal Processing, 150, 10.1016/j.ymssp.2020.107294 · DOI
  63. Gardner, 2020, Bayesian history matching for structural dynamics applications, Mechanical Systems and Signal Processing, 143, 10.1016/j.ymssp.2020.106828 · DOI
  64. Ding, 2019, Structural damage identification using improved Jaya algorithm based on sparse regularization and Bayesian inference, Mechanical Systems and Signal Processing, 132, 211, 10.1016/j.ymssp.2019.06.029 · DOI
  65. Rocchetta, 2018, On-line Bayesian model updating for structural health monitoring, Mechanical Systems and Signal Processing, 103, 174, 10.1016/j.ymssp.2017.10.015 · DOI
  66. Zhou, 2019, Mobile sensing of point-source gas emissions using bayesian inference: An empirical examination of the likelihood function, Atmospheric Environment, 218, 10.1016/j.atmosenv.2019.116981 · DOI
  67. Yee, 1993, Statistical characteristics of concentration fluctuations in dispersing plumes in the atmospheric surface layer, Boundary-Layer Meteorology, 65, 69, 10.1007/BF00708819 · DOI
  68. Senocak, 2008, Stochastic event reconstruction of atmospheric contaminant dispersion using Bayesian inference, Atmospheric Environment, 42, 7718, 10.1016/j.atmosenv.2008.05.024 · DOI
  69. Wade, 2013, Stochastic reconstruction of multiple source atmospheric contaminant dispersion events, Atmospheric Environment, 74, 45, 10.1016/j.atmosenv.2013.02.051 · DOI
  70. Bi, 2019, The role of the Bhattacharyya distance in stochastic model updating, Mechanical Systems and Signal Processing, 117, 437, 10.1016/j.ymssp.2018.08.017 · DOI
  71. Abdessalem, 2018, Model selection and parameter estimation in structural dynamics using approximate Bayesian computation, Mechanical Systems and Signal Processing, 99, 306, 10.1016/j.ymssp.2017.06.017 · DOI
  72. Fang, 2019, Probabilistic damage identification incorporating approximate Bayesian computation with stochastic response surface, Mechanical Systems and Signal Processing, 128, 229, 10.1016/j.ymssp.2019.03.044 · DOI
  73. Scheidt, 2009, Uncertainty quantification in reservoir performance using distances and kernel methods – application to a West Africa deepwater turbidite reservoir, SPE Journal, 14, 680, 10.2118/118740-PA · DOI
  74. Reprint of: Mahalanobis, P.C. (1936) “On the Generalised Distance in Statistics”, Sankhya A 80 (2018) 1–7. doi:10.1007/s13171-019-00164-5. · DOI
  75. Bhattacharyya, 1946, On a measure of divergence between two multinomial populations, The Indian Journal of Statistics, 7, 401
  76. Diaconis, 1979, Conjugate priors for exponential families, The Annals of Statistics, 7, 269, 10.1214/aos/1176344611 · DOI
  77. Brown, 1986, Fundamentals of Statistical Exponential Families: With Applications in Statistical Decision Theory, Institute of Mathematical Statistics
  78. Casella, 1990, Statistical Inference, Brooks/Cole Publishing Company
  79. Baldacchino, 2017, Robust nonlinear system identification: Bayesian mixture of experts using the t-distribution, Mechanical Systems and Signal Processing, 85, 977, 10.1016/j.ymssp.2016.08.045 · DOI
  80. Pharr, 2017, Monte Carlo Integration, Physically Based Rendering, 747, 10.1016/B978-0-12-800645-0.50013-0 · DOI
  81. Berger, 2015, Estimation: Point and interval, International Encyclopedia of the Social and Behavioral Sciences, 16, 10.1016/B978-0-08-097086-8.42026-X · DOI
  82. Green, 2015, Bayesian and Markov chain Monte Carlo methods for identifying nonlinear systems in the presence of uncertainty, Philosophical Transactions of the Royal Society A, 373
  83. Hadjidoukas, 2014, Bayesian uncertainty quantification and propagation for discrete element simulations of granular materials, Computer Methods in Applied Mechanics and Engineering, 282, 218, 10.1016/j.cma.2014.07.017 · DOI
  84. Rastogi, 2016, Fatigue crack growth prediction in nuclear piping using Markov Chain Monte Carlo simulation, Fatigue and Fracture of Engineering Materials and Structures, 40, 145, 10.1111/ffe.12486 · DOI
  85. Rouchier, 2019, Sequential Monte Carlo for on-line parameter estimation of a lumped building energy model, Energy and Buildings, 187, 86, 10.1016/j.enbuild.2019.01.045 · DOI
  86. Jensen, 2019, A Bayesian model updating approach for detection-related problems in water distribution networks, Reliability Engineering and System Safety, 185, 100, 10.1016/j.ress.2018.12.014 · DOI
  87. Straub, 2014, Bayesian updating with structural reliability methods, Journal of Engineering Mechanics, 141
  88. Metropolis, 1953, Equation of state calculations by fast computing machines, The Journal of Chemical Physics, 21, 1087, 10.1063/1.1699114 · DOI · Paper page
  89. Kruschke, 2015, Markov Chain Monte Carlo, Doing Bayesian Data Analysis, 143, 10.1016/B978-0-12-405888-0.00007-6 · DOI
  90. Hastings, 1970, Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97, 10.1093/biomet/57.1.97 · DOI · Paper page
  91. Gelfand, 1990, Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association, 85, 398, 10.1080/01621459.1990.10476213 · DOI · Paper page
  92. Gilks, 1992, Adaptive rejection sampling for gibbs sampling, Applied Statistics, 41, 337, 10.2307/2347565 · DOI
  93. Chib, 2001, Markov Chain Monte Carlo methods: computation and inference, Handbook of Econometrics, 5, 3569, 10.1016/S1573-4412(01)05010-3 · DOI
  94. Wilkinson, 2005, Parallel Bayesian computation, Handbook of Parallel Computing and Statistics, 481
  95. Brockwell, 2006, Parallel Markov Chain Monte Carlo simulation by pre-fetching, Journal of Computational and Graphical Statistics, 15, 246, 10.1198/106186006X100579 · DOI
  96. Neal, 2003, Slice sampling, The Annals of Statistics, 31, 705, 10.1214/aos/1056562461 · DOI
  97. Duane, 1987, Hybrid Monte Carlo, Physics Letters B, 195, 216, 10.1016/0370-2693(87)91197-X · DOI
  98. Girolami, 2011, Riemann manifold Langevin and Hamiltonian Monte Carlo methods, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 73, 123, 10.1111/j.1467-9868.2010.00765.x · DOI · Paper page
  99. Besag, 1994, Comments on ”Representations of Knowledge in Complex Systems” by U. Grenander and M. I. miller, Journal of the Royal Statistical Society, Series B, 56, 591
  100. Roberts, 1996, Exponential convergence of langevin distributions and their discrete approximations, Bernoulli, 2, 341, 10.2307/3318418 · DOI
  101. Roberts, 1998, Optimal scaling of discrete approximations to Langevin diffusions, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 60, 255, 10.1111/1467-9868.00123 · DOI
  102. Liu, 2000, The multiple-try method and local optimization in metropolis sampling, Journal of the American Statistical Association, 95, 121, 10.1080/01621459.2000.10473908 · DOI
  103. Martino, 2018, A review of multiple try MCMC algorithms for signal processing, Digital Signal Processing, 75, 134, 10.1016/j.dsp.2018.01.004 · DOI
  104. Green, 1995, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination, Biometrika, 82, 711, 10.1093/biomet/82.4.711 · DOI
  105. Andrieu, 2009, The pseudo-marginal approach for efficient Monte Carlo computations, The Annals of Statistics, 37, 697, 10.1214/07-AOS574 · DOI
  106. Roberts, 2001, Optimal scaling for various metropolis-hastings algorithms, Statistical Science, 16, 351, 10.1214/ss/1015346320 · DOI
  107. Roberts, 1997, Weak convergence and optimal scaling of random walk metropolis algorithms, The Annals of Applied Probability, 7, 110
  108. M. Bedard, Optimal acceptance rates for metropolis algorithms: Moving beyond 0.234, Stochastic Processes and Their Applications 118 (2008) 2198–2222. doi:10.1016/j.spa.2007.12.005. · DOI
  109. Chib, 1995, Understanding the metropolis-hastings algorithm, Journal of Engineering Mechanics, 49, 327
  110. Costilla, 2019, Bayesian model-based clustering for longitudinal ordinal data, Computational Statistics, 34, 1015, 10.1007/s00180-019-00872-4 · DOI
  111. Johnson, 2013, Component-wise Markov Chain Monte Carlo: Uniform and geometric ergodicity under mixing and composition, Statistical Science, 28, 360, 10.1214/13-STS423 · DOI
  112. Haario, 2005, Componentwise adaptation for high dimensional MCMC, Computational Statistics, 20, 265, 10.1007/BF02789703 · DOI
  113. Levine, 2005, Implementing componentwise Hastings algorithms, Computational Statistics and Data Analysis, 48, 363, 10.1016/j.csda.2004.02.002 · DOI
  114. Casella, 1992, Explaining the gibbs sampler, The American Statistician, 46, 167, 10.1080/00031305.1992.10475878 · DOI
  115. Honarmandi, 2020, Bayesian probabilistic prediction of precipitation behavior in Ni-Ti shape memory alloys, Computational Materials Science, 172, 10.1016/j.commatsci.2019.109334 · DOI
  116. Kawahara, 2018, Unique fitting of electrochemical impedance spectra by random walk Metropolis Hastings algorithm, Journal of Power Sources, 403, 184, 10.1016/j.jpowsour.2018.09.091 · DOI
  117. Wang, 2017, Bayesian forecasting and uncertainty quantifying of stream flows using Metropolis-Hastings Markov Chain Monte Carlo algorithm, Journal of Hydrology, 549, 476, 10.1016/j.jhydrol.2017.03.073 · DOI
  118. Grišins, 2014, Metropolis-Hastings thermal state sampling for numerical simulations of Bose-Einstein condensates, Computer Physics Communications, 185, 1926, 10.1016/j.cpc.2014.03.021 · DOI
  119. Biswal, 2017, Finite element model updating of concrete structures based on imprecise probability, Mechanical Systems and Signal Processing, 94, 165, 10.1016/j.ymssp.2017.02.042 · DOI
  120. Green, 2015, Bayesian system identification of dynamical systems using highly informative training data, Mechanical Systems and Signal Processing, 56–57, 109, 10.1016/j.ymssp.2014.10.003 · DOI
  121. Beck, 2002, Bayesian updating of structural models and reliability using Markov chain Monte Carlo simulation, Journal of Engineering Mechanics, 128, 380, 10.1061/(ASCE)0733-9399(2002)128:4(380) · DOI
  122. Betz, 2016, Transitional Markov chain Monte Carlo: observations and improvements, Journal of Engineering Mechanics, 142, 10.1061/(ASCE)EM.1943-7889.0001066 · DOI
  123. Efraimidis, 2008, Weighted random sampling, Encyclopedia of Algorithms, 1024, 10.1007/978-0-387-30162-4_478 · DOI · Paper page
  124. Ching, 2016, Application of the transitional Markov chain Monte Carlo algorithm to probabilistic site characterization, Engineering Geology, 203, 151, 10.1016/j.enggeo.2015.10.015 · DOI
  125. Ortiz, 2015, Identification of Bouc–wen type models using the transitional Markov chain Monte Carlo method, Computers and Structures, 146, 252, 10.1016/j.compstruc.2014.10.012 · DOI
  126. Zhou, 2018, Model updating and uncertainty analysis for creep behavior of soft soil, Computers and Geotechnics, 100, 135, 10.1016/j.compgeo.2018.04.006 · DOI
  127. Wang, 2014, Reliability-based optimal design of linear structures subjected to stochastic excitations, Structural Safety, 47, 29, 10.1016/j.strusafe.2013.11.002 · DOI
  128. Gomes, 2014, Model Updating by Uncertain Parameter Inference, Vulnerability, Uncertainty, and Risk
  129. Sedehi, 2019, Probabilistic hierarchical bayesian framework for time-domain model updating and robust predictions, Mechanical Systems and Signal Processing, 123, 648, 10.1016/j.ymssp.2018.09.041 · DOI
  130. Gordon, 1993, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F (Radar and Signal Processing), 140, 107, 10.1049/ip-f-2.1993.0015 · DOI
  131. Moral, 1997, Nonlinear filtering: Interacting particle resolution, Comptes Rendus De LAcadémie Des Sciences – Series I – Mathematics, 325, 653
  132. Liu, 1998, Sequential Monte Carlo methods for dynamic systems, Journal of the American Statistical Association, 93, 1032, 10.1080/01621459.1998.10473765 · DOI
  133. Arulampalam, 2002, A tutorial on particle filters for on-line nonlinear/non-gaussian bayesian tracking, IEEE Transactions on Signal Processing, 50, 174, 10.1109/78.978374 · DOI
  134. Cappe, 2007, An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo, Proceedings of the IEEE, 95, 899, 10.1109/JPROC.2007.893250 · DOI
  135. Rogers, 2020, Bayesian Solutions to State-Space Structural Identification, Model Validation and Uncertainty Quantification, 3, 247
  136. Rogers, 2019, Identification of a Duffing oscillator using particle Gibbs with ancestor sampling, Journal of Physics: Conference Series, 1264
  137. Moral, 2006, Sequential Monte Carlo samplers, Journal of the Royal Statistical Society. Series B (Statistical Methodology), 68, 411, 10.1111/j.1467-9868.2006.00553.x · DOI · Paper page
  138. Beer, 2014, Emerging Concepts and Approaches for Efficient and Realistic Uncertainty Quantification, Maintenance and Safety of Aging Infrastructure, 121
  139. Doucet, 2006, Efficient block sampling strategies for sequential Monte Carlo methods, Journal of Computational and Graphical Statistics, 15, 693, 10.1198/106186006X142744 · DOI
  140. Moral, 2012, On adaptive resampling strategies for sequential Monte Carlo methods, Bernoulli, 18, 252
  141. Jasra, 2010, Inference for Lévy-Driven stochastic volatility models via adaptive sequential Monte Carlo, Scandinavian Journal of Statistics, 38, 1, 10.1111/j.1467-9469.2010.00723.x · DOI
  142. C.A. Naesseth, F. Lindsten, T.B. Schön, Nested Sequential Monte Carlo Methods, Proceedings of the 32nd International Conference on International Conference on Machine Learning 37 (2015) 1292–1301.
  143. Naesseth, 2019, High-dimensional filtering using nested sequential Monte Carlo, IEEE Transactions on Signal Processing, 67, 4177, 10.1109/TSP.2019.2926035 · DOI
  144. Hammersley, 1964, 10.1007/978-94-009-5819-7 · DOI
  145. Theodoridis, 2015, Monte Carlo Methods, Machine Learning, 707
  146. Kong, 1994, Sequential imputations and Bayesian missing data problems, Journal of the American Statistical Association, 89, 278, 10.1080/01621459.1994.10476469 · DOI
  147. Li, 2004, The sampling/importance resampling algorithm, applied Bayesian modeling and causal inference from incomplete-data, Perspectives, 265
  148. West, 1993, Approximating posterior distributions by mixtures, Journal of the Royal Statistical Society: Series B (Methodological), 55, 409, 10.1111/j.2517-6161.1993.tb01911.x · DOI
  149. Chopin, 2002, A sequential particle filter method for static models, Biometrika, 89, 539, 10.1093/biomet/89.3.539 · DOI
  150. Basaran, 2018, Multiresolution alignment for multiple unsynchronized audio sequences using sequential Monte Carlo samplers, Digital Signal Processing, 77, 77, 10.1016/j.dsp.2017.10.024 · DOI
  151. Schön, 2018, Probabilistic Learning of Nonlinear Dynamical Systems using Sequential Monte Carlo Mechanical Systems and Signal Processing, 104, 866
  152. Zhu, 2018, A new moving strategy for the Sequential Monte Carlo approach in optimizing the hydrological model parameters, Advances in Water Resources, 114, 164, 10.1016/j.advwatres.2018.02.007 · DOI
  153. Sun, 2014, Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model, Mechanical Systems and Signal Processing, 45, 396, 10.1016/j.ymssp.2013.08.022 · DOI
  154. Svensson, 2018, Learning of state-space models with highly informative observations: A tempered sequential Monte Carlo solution, Mechanical Systems and Signal Processing, 104, 915, 10.1016/j.ymssp.2017.09.016 · DOI
  155. Wang, 2015, A general sequential Monte Carlo method based optimal wavelet filter: A Bayesian approach for extracting bearing fault features, Mechanical Systems and Signal Processing, 52–53, 293, 10.1016/j.ymssp.2014.07.005 · DOI
  156. Pisal, 2016, Dynamic response of structure with tuned mass friction damper, International Journal of Advanced Structural Engineering, 8, 363, 10.1007/s40091-016-0136-7 · DOI
  157. Beck, 2002, Bayesian updating of structural models and reliability using Markov Chain Monte Carlo simulation, Journal of Engineering Mechanics, 128, 380, 10.1061/(ASCE)0733-9399(2002)128:4(380) · DOI
  158. Govers, 2015, A comparison of two stochastic model updating methods using the DLR-AIRMOD test structure, Mechanical Systems and Signal Processing, 52–53, 105, 10.1016/j.ymssp.2014.06.003 · DOI
  159. Broggi, 2017, Comparison of Bayesian and interval uncertainty quantification: Application to the AIRMOD test structure, 2017 IEEE Symposium Series on Computational Intelligence (SSCI), 10.1109/SSCI.2017.8280882 · DOI
  160. Patelli, 2017, Sensitivity or Bayesian model updating: A comparison of techniques using the DLR-AIRMOD test data, Archive of Applied Mechanics, 87, 905, 10.1007/s00419-017-1233-1 · DOI
  161. Mottershead, 2011, The sensitivity method in finite element model updating: A tutorial, Mechanical Systems and Signal Processing, 25, 2275, 10.1016/j.ymssp.2010.10.012 · DOI
  162. Mottershead, 1993, Model updating in structural dynamics: A survey, Journal of Sound and Vibration, 167, 347, 10.1006/jsvi.1993.1340 · DOI
  163. Patelli, 2017, COSSAN: A Multidisciplinary Software Suite for Uncertainty Quantification and Risk Management, Handbook of Uncertainty Quantification, 1909, 10.1007/978-3-319-12385-1_59 · DOI
  164. Patelli, 2014, OpenCossan: An Efficient Open Tool for Dealing with Epistemic and Aleatory Uncertainties, Vulnerability, Uncertainty, and Risk
  165. G. Marsaglia, W.W. Tsang, J. Wang, Evaluating Kolmogorovs Distribution, Journal of Statistical Software 8. doi:10.18637/jss.v008.i18. · DOI
  166. M.A. Stephens, Introduction to Kolmogorov (1933) On the Empirical Determination of a Distribution, Springer Series in Statistics Breakthroughs in Statistics (1992) 93–105. doi:10.1007/978-1-4612-4380-9_9. · DOI
  167. Au, 2003, Important sampling in high dimensions, Structural Safety, 25, 139, 10.1016/S0167-4730(02)00047-4 · DOI
  168. Snyder, 2008, Obstacles to high-dimensional particle filtering, Monthly Weather Review, 136, 4629, 10.1175/2008MWR2529.1 · DOI
  169. Rebeschini, 2015, Can local particle filters beat the curse of dimensionality?, The Annals of Applied Probability, 25, 2809, 10.1214/14-AAP1061 · DOI
  170. Roberts, 2007, Coupling and ergodicity of adaptive Markov Chain Monte Carlo Algorithms, Journal of Applied Probability, 44, 458, 10.1239/jap/1183667414 · DOI
  171. Roberts, 2009, Examples of Adaptive MCMC, Journal of Computational and Graphical Statistics, 18, 349, 10.1198/jcgs.2009.06134 · DOI
  172. Lam, 2015, Bayesian model updating of a coupled-slab system using field test data utilizing an enhanced Markov Chain Monte Carlo simulation algorithm, Engineering Structures, 102, 144, 10.1016/j.engstruct.2015.08.005 · DOI
  173. Kantas, 2014, Sequential Monte Carlo methods for high-dimensional inverse problems: a case study for the Navier-Stokes equations, SIAM/ASA Journal on Uncertainty Quantification, 2, 464, 10.1137/130930364 · DOI
  174. Vo, 2019, Bayesian estimation for stochastic gene expression using multifidelity models, The Journal of Physical Chemistry B, 123, 2217, 10.1021/acs.jpcb.8b10946 · DOI
  175. Chatzis, 2017, Online Bayesian identification of non-smooth systems, Procedia Engineering, 199, 918, 10.1016/j.proeng.2017.09.238 · DOI
  176. Nakajima, 2011, Bayesian analysis of time-varying parameter vector autoregressive model for the Japanese economy and monetary policy, Journal of the Japanese and International Economies, 25, 225, 10.1016/j.jjie.2011.07.004 · DOI
  177. Dimitrakopoulos, 2017, Semiparametric bayesian inference for time-varying parameter regression models with stochastic volatility, Economics Letters, 150, 10, 10.1016/j.econlet.2016.10.035 · DOI
  178. Mallapur, 2019, Uncertainty quantification in the mathematical modelling of a suspension strut using Bayesian inference, Mechanical Systems and Signal Processing, 118, 158, 10.1016/j.ymssp.2018.08.046 · DOI
  179. Fuentes, 2019, Efficient parameter identification and model selection in nonlinear dynamical systems via sparse Bayesian learning, Journal of Physics: Conference Series, 1264

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Metadata

Authors
Lye, A., Cicirello, A., & Patelli, E.
Venue
Mechanical Systems and Signal Processing
Publisher
Elsevier BV
Published
2021-10
Volume
159
Pages
107760
ISSN
0888-3270
DOI
10.1016/j.ymssp.2021.107760
Cited by
169
References
3
Language
en
Licence
Funders
EPSRC

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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