Bayesian Filtering and Smoothing by Simo Särkkä

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  • Title: Bayesian Filtering and Smoothing
  • Author: Simo Särkkä
  • Category: Computer systems
  • Date: September 2013
  • Lenguage: English
  • Pages: unknown
  • ISBN: 9781107030657

 

Ebook description:

Filtering and smoothing strategies are used to supply an correct estimate of the state of a time-varying system based mostly on a number of observational inputs (information). Curiosity in these strategies has exploded lately, with quite a few functions rising in fields comparable to navigation, aerospace engineering, telecommunications and drugs. This compact, casual introduction for graduate college students and superior undergraduates presents the present state-of-the-art filtering and smoothing strategies in a unified Bayesian framework. Readers study what non-linear Kalman filters and particle filters are, how they’re associated, and their relative benefits and downsides. In addition they uncover how state-of-the-art Bayesian parameter estimation strategies will be mixed with state-of-the-art filtering and smoothing algorithms. The guide’s sensible and algorithmic method assumes solely modest mathematical stipulations. Examples embody Matlab computations, and the quite a few end-of-chapter workout routines embody computational assignments. Matlab code is offered for download at www.cambridge.org/sarkka, selling hands-on work with the strategies

 

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