Bayesian Filtering and Smoothing (Paperback, 2nd Revised edition)

Now in its second edition, this accessible text presents a unified Bayesian treatment of state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models. The book focuses on discrete-time state space models and carefully introduces fundamental aspects related to optimal filtering and smoothing. In particular, it covers a range of efficient non-linear Gaussian filtering and smoothing algorithms, as well as Monte Carlo-based algorithms. This updated edition features new chapters on constructing state space models of practical systems, the discretization of continuous-time state space models, Gaussian filtering by enabling approximations, posterior linearization filtering, and the corresponding smoothers. Coverage of key topics is expanded, including extended Kalman filtering and smoothing, and parameter estimation. The book's practical, algorithmic approach assumes only modest mathematical prerequisites, suitable for graduate and advanced undergraduate students. Many examples are included, with Matlab and Python code available online, enabling readers to implement algorithms in their own projects.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Series:	Institute of Mathematical Statistics Textbooks
Release date:	May 2023
Authors:	Simo Särkkä • Lennart Svensson
Format:	Paperback
Pages:	430
Edition:	2nd Revised edition
ISBN-13:	978-1-108-92664-5
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	1-108-92664-9
Barcode:	9781108926645

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