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Model selection and parameter estimation for an improved approximate Bayesian computation sequential Monte Carlo algorithm. (English) Zbl 1497.65007

MSC:

65C05 Monte Carlo methods
62F15 Bayesian inference

References:

[1] Muto, M.; Beck, J. L., Bayesian updating and model class selection for hysteretic structural models using stochastic simulation, Journal of Vibration and Control, 14, 1C2, 7C34 (2008) · Zbl 1229.74065 · doi:10.1177/1077546307079400
[2] Zrate, B. A.; Caicedo, J. M.; Yu, J.; Ziehl, P., Bayesian model updating and prognosis of fatigue crack growth, Engineering Structures, 45 (2012) · doi:10.1016/j.engstruct.2012.06.012
[3] Bisaillon, Ph.; Sandhu, R.; Khalil, M.; Pettit, C.; Poirel, D.; Sarkar, A., Bayesian parameter estimation and model selection for strongly nonlinear dynamical systems, Nonlinear Dynamics, 82, 3, 1061-1080 (2015) · Zbl 1437.62106 · doi:10.1007/s11071-015-2217-8
[4] Beck, J. L.; Yuen, K. V., Model selection using response measurements: bayesian probabilistic approach, Journal of Engineering Mechanics, 130, 2, 192-203 (2004) · doi:10.1061/(asce)0733-9399(2004)130:2(192)
[5] Sandhu, R.; Khalil, M.; Sarkar, A.; Poirel, D., Bayesian model selection for nonlinear aeroelastic systems using wind-tunnel data, Computer Methods in Applied Mechanics and Engineering, 282, 161-183 (2014) · Zbl 1423.76186 · doi:10.1016/j.cma.2014.06.013
[6] Ritto, T. G.; Nunes, L. C. S., Bayesian model selection of hyperelastic models for simple and pure shear at large deformations, Computers & Structures, 156, 101-109 (2015) · doi:10.1016/j.compstruc.2015.04.008
[7] Liu, W.; Tang, S.; Xiao, Y., Model selection and evaluation based on emerging infectious disease data sets including A/H1N1 and ebola, Computational and Mathematical Methods in Medicine, 2015 (2015) · Zbl 1335.92097 · doi:10.1155/2015/207105
[8] Cadini, F.; Sbarufatti, C.; Corbetta, M.; Giglio, M., A particle filter-based model selection algorithm for fatigue damage identification on aeronautical structures, Structural Control and Health Monitoring, 24, 11 (2017) · doi:10.1002/stc.2002
[9] Skilling, J.; Fischer, R.; Preuss, R.; Toussaint, U. V., Nested Sampling (2004), New York, NY, USA: American Institute of Physics Conference Series, New York, NY, USA
[10] Skilling, J., Nested sampling for general Bayesian computation, Bayesian Anal, 1, 4 (2006) · Zbl 1332.62374 · doi:10.1214/06-ba127
[11] Pritchard, J. K.; Seielstad, M. T.; Perez-Lezaun, A.; Feldman, M. W., Population growth of human Y chromosomes: a study of Y chromosome microsatellites, Molecular Biology and Evolution, 16, 12, 1791-1798 (1999) · doi:10.1093/oxfordjournals.molbev.a026091
[12] Ben Abdessalem, A.; Dervilis, N.; Wagg, D.; Worden, K., Model selection and parameter estimation in structural dynamics using approximate Bayesian computation, Mechanical Systems and Signal Processing, 99, 306-325 (2018) · doi:10.1016/j.ymssp.2017.06.017
[13] Toni, T.; Welch, D.; Strelkowa, N.; Ipsen, A.; Stumpf, M. P., Approximate bayesian computation scheme for parameter inference and model selection in dynamical systems, Journal of The Royal Society Interface, 6, 31, 187-202 (2009) · doi:10.1098/rsif.2008.0172
[14] Rodrigues, G. S.; Prangle, D.; Sisson, S. A., Recalibration: a post-processing method for approximate Bayesian computation, Computational Statistics & Data Analysis, 126, 53-66 (2018) · Zbl 1469.62133 · doi:10.1016/j.csda.2018.04.004
[15] Spiegelhalter, D. J.; Best, N. G.; Carlin, B. P.; van der Linde, A., Bayesian measures of model complexity and fit, Journal of the Royal Statistical Society: Series B, 64, 4, 583-639 (2002) · Zbl 1067.62010 · doi:10.1111/1467-9868.00353
[16] Stephens, P. A.; Buskirk, S. W.; Hayward, G. D.; Del Rio, C. M., A call for statistical pluralism answered, Journal of Applied Ecology, 44, 2, 461-463 (2007) · doi:10.1111/j.1365-2664.2007.01302.x
[17] Link, W. A.; Barker, R. J., Model weights and the foundations of multimodel inference, Ecology, 87, 10, 2626-2635 (2006) · doi:10.1890/0012-9658(2006)87[2626:mwatfo]2.0.co;2
[18] Congdon, P., Bayesian model choice based on Monte Carlo estimates of posterior model probabilities, Computational Statistics & Data Analysis, 50, 2, 346-357 (2006) · Zbl 1330.62115 · doi:10.1016/j.csda.2004.08.001
[19] Llorente, F.; Martino, L.; Delgado, D.; Lopez-Santiago, J., Marginal Likelihood Computation for Model Selection and Hypothesis Testing: An Extensive Review (2020), https://arxiv.org/abs/2005.08334
[20] Llorente, F.; Martino, L.; Cuberlo, E.; Lopez-Santiago, J.; Delgado, D., On the safe use of prior densities for Bayesian model selection, viXra, 2110 (2021)
[21] Boys, R. J.; Wilkinson, D. J.; Kirkwood, T. B. L., Bayesian inference for a discretely observed stochastic kinetic model, Statistics and Computing, 18, 2, 125-135 (2008) · doi:10.1007/s11222-007-9043-x
[22] Golightly, A.; Wilkinson, D. J., Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo, Interface Focus, 1, 6, 807-820 (2011) · doi:10.1098/rsfs.2011.0047
[23] Sisson, S. A.; Fan, Y.; Tanaka, M. M., Sequential Monte Carlo without likelihoods, Proceedings of the National Academy of Sciences, 104, 6, 1760-1765 (2007) · Zbl 1160.65005 · doi:10.1073/pnas.0607208104
[24] World Health Organization, Accelerating Work to Overcome the Globalimpact of Neglected Tropical Diseases C a Roadmap for Implementation (2012), Geneva, Switzerland
[25] Akaike, H., Information theory as an extension of the maximum likelihood principle, Second International Symposium on Information Theory (1973), Hungary, Europe: Akademiai Kiado, Hungary, Europe · Zbl 0283.62006
[26] Burnham, K. P.; Anderson, D. R., Model Selection and Multimodel Inference (2002), New York, NY, USA: Springer, New York, NY, USA · Zbl 1005.62007
[27] Garamszegi, L. Z., Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction, Behavioral Ecology and Sociobiology, 65, 1, 1-11 (2010) · doi:10.1007/s00265-010-1028-7
[28] Verhulst, P. F., Notice sur la loi que la population suit dans son accroissement. correspondance mathematique et physique publiee par a quetelet, brussels, Quetelet, 10, 10 (1838)
[29] Winsor, C. P., The gompertz curve as a growth curve, Proceedings of the National Academy of Sciences, 18, 1, 1-8 (1932) · JFM 58.0572.01 · doi:10.1073/pnas.18.1.1
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