Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

Dublin Core

Title

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

Subject

Computer Science

Description

By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems.

Creator

Huber, Marco

Source

https://www.ksp.kit.edu/9783731503385

Publisher

KIT Scientific Publishing

Date

2015

Contributor

Baihaqi

Rights

Creative Commons

Format

PDF

Language

English

Type

Textbooks

Files

Citation

Huber, Marco, “Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications,” Open Educational Resource (OER) - Unsyiah Library, accessed October 4, 2022, http://uilis.unsyiah.ac.id/oer/items/show/4591.

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