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Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

Habilitationsschrift

Autori

Marco Huber

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Pagine: 304pagine
Tempo di lettura: 11ore

Matematica

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The thesis explores the optimal Bayesian filtering problem by focusing on Gaussian distributions, enabling the development of computationally efficient algorithms. It addresses three specific scenarios: filtering using only Gaussian distributions, employing Gaussian mixture filtering for handling strong nonlinearities, and utilizing Gaussian process filtering in data-driven contexts. For each scenario, the author derives effective algorithms and demonstrates their application to real-world challenges, highlighting the practical implications of these methods in various domains.

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Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications, Marco Huber

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Pubblicato: 2015
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Titolo: Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications
Sottotitolo: Habilitationsschrift
Lingua: Inglese
Autori: Marco Huber
Editore: Karlsruher Institut für Technologie
Pubblicato: 2015
Formato: In brossura
Pagine: 304
ISBN13: 9783731503385
Tag: Matematica
Descrizione: The thesis explores the optimal Bayesian filtering problem by focusing on Gaussian distributions, enabling the development of computationally efficient algorithms. It addresses three specific scenarios: filtering using only Gaussian distributions, employing Gaussian mixture filtering for handling strong nonlinearities, and utilizing Gaussian process filtering in data-driven contexts. For each scenario, the author derives effective algorithms and demonstrates their application to real-world challenges, highlighting the practical implications of these methods in various domains.