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Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal Posterior Distributions Evaluation
[article]
2022
arXiv
pre-print
Statistical model updating is frequently used in engineering to calculate the uncertainty of some unknown latent parameters when a set of measurements on observable quantities is given. Variational inference is an alternative approach to sampling methods that has been developed by the machine learning community to estimate posterior approximations through an optimization approach. In this paper, the Variational Bayesian Monte Carlo (VBMC) method is investigated with the purpose of dealing with
arXiv:2202.11645v1
fatcat:vmawn6l2k5e7dbowzje77h2kkm