Titlebook: Regularized System Identification; Learning Dynamic Mod Gianluigi Pillonetto,Tianshi Chen,Lennart Ljung Book‘‘‘‘‘‘‘‘ 2022 The Editor(s) (if

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Classical System Identification,s and experience has been developed since then. Indeed, there is a very extensive literature on the subject, with many text books, like [., ., .]. Some main points of this “classical” field are summarized in this chapter, just pointing to the basic structure of the problem area. The problem centres

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Regularization of Linear Regression Models,sy to fit and enjoy simple analytical properties. The simplest method to fit linear regression models is least squares whose systematic treatment is available in many textbooks, e.g., [., Chap. 4], [.]. Linear regression models can be fitted also in different way and a class of methods that we will

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Bayesian Interpretation of Regularization,cient to obtain a precise estimate of the unknown parameter vector and standard methods, such as least squares, yield poor solutions. The fact itself that an estimate is regarded as poor suggests the existence of some form of prior knowledge on the degree of acceptability of candidate solutions. It