Abstract

This tutorial gives a basic yet rigorous introduction to observable operator models (OOMs). OOMs are a recently discovered class of models of stochastic processes. They are mathematically simple in that they require only concepts from elementary linear algebra. The linear algebra nature gives rise to an e#cient, consistent, unbiased, constructive learning procedure for estimating models from empirical data. The tutorial describes in detail the mathematical foundations and the practical use of OOMs for identifying and predicting discrete-time, discrete-valued processes, both for output-only and input-output systems. key words: stochastic time series, system identification, observable operator models Zusammenfassung Dies Tutorial bietet eine grundliche Einfuhrung in observable operator Modelle (OOMs). OOMs sind eine kurzlich entdeckte Klasse von Modellen stochastischer Prozesse. Sie sind mit den Mitteln der elementaren linearen Algebra darzustellen. Die Einfachheit der Darstellung fuhrt...

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