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Bayesian Reasoning and Machine Learning by David Barber

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25

Switching linear dynamical systems

Hidden Markov models assume that the underlying process is discrete; linear dynamical systems that the underlying process is continuous. However, there are scenarios in which the underlying system might jump from one continuous regime to another. In this chapter we discuss a class of models that can be used in this situation. Unfortunately the technical demands of this class of models are somewhat more involved than in previous chapters, although the models are correspondingly more powerful.

25.1   Introduction

Complex timeseries which are not well described globally by a single linear dynamical system may be divided into segments, each modelled by a potentially different LDS. Such models can handle situations ...

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