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State Space Models

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Bayesian Inference of State Space Models

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Abstract

This chapter introduces state space models and provides some motivating examples. Linear Gaussian and non-linear, non-Gaussian models are introduced. Examples include linear trend and seasonal time series, time-varying regression, bearings-only tracking, financial time series and systems identification state space models. The chapter sets the stage for the book and provides a chapter-by-chapter description of the book. The chapter includes a brief historical overview of the developments that led to the discovery of the Kalman filter and the research directions in the wider field of state space modelling.

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Authors and Affiliations

  1. School of Mathematics, University of Sheffield, Sheffield, UK

    Kostas Triantafyllopoulos

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  1. Kostas Triantafyllopoulos
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Triantafyllopoulos, K. (2021). State Space Models. In: Bayesian Inference of State Space Models. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-76124-0_1

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