Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cyclical Variational Bayes Monte Carlo for Efficient Multi-Modal
  Posterior Distributions Evaluation   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 statistical model updating
problems in engineering involving expensive-to-run models. This method combines
the active-sampling Bayesian quadrature with a Gaussian-process based
variational inference to yield a non-parametric estimation of the posterior
distribution of the identified parameters involving few runs of the
expensive-to-run model. VBMC can also be used for model selection as it
produces an estimation of the model's evidence lower bound. In this paper, a
variant of the VBMC algorithm is developed through the introduction of a
cyclical annealing schedule into the algorithm. The proposed cyclical VBMC
algorithm allows to deal effectively with multi-modal posteriors by having
multiple cycles of exploration and exploitation phases. Four numerical examples
are used to compare the standard VBMC algorithm, the monotonic VBMC, the
cyclical VBMC and the Transitional Ensemble Markov Chain Monte Carlo (TEMCMC).
Overall, it is found that the proposed cyclical VBMC approach yields accurate
results with a very reduced number of model runs compared to the state of the
art sampling technique TEMCMC. In the presence of potential multi-modal
problems, the proposed cyclical VBMC algorithm outperforms all the other
approaches in terms of accuracy of the resulting posterior.

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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-ph
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex