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Bayesian Inference

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Operational Modal Analysis

Abstract

This chapter introduces the Bayesian approach for system identification in a general context. The concept of ‘identifiability’ is introduced, which affects how computations should be performed for making inference. Computational strategies for ‘globally identifiable’, ‘local identifiable’ and ‘unidentifiable’ situations are further discussed in individual sections. ‘Model class selection’ is also introduced, which is an important topic for validating models beyond system identification.

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Authors and Affiliations

  1. Center for Engineering Dynamics and Institute for Risk and Uncertainty, University of Liverpool, Liverpool, UK

    Siu-Kui Au

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  1. Siu-Kui Au
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Correspondence to Siu-Kui Au .

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Au, SK. (2017). Bayesian Inference. In: Operational Modal Analysis. Springer, Singapore. https://doi.org/10.1007/978-981-10-4118-1_8

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  • DOI: https://doi.org/10.1007/978-981-10-4118-1_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4117-4

  • Online ISBN: 978-981-10-4118-1

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