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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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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