Bayesian Filtering and Smoothing

Simo Särkkä

ISBN 10: 1108926649 / ISBN 13: 9781108926645

Published by Cambridge University Press Jun 2023, 2023

About this Item

Neuware - 'Now in its second edition, this accessible text presents a unified Bayesian treatment of the 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 the Matlab and Python code available online, enabling readers to implement the algorithms in their own projects'--.

Seller Inventory # 9781108926645

Bibliographic Details

• Title: Bayesian Filtering and Smoothing
• Publisher: Cambridge University Press Jun 2023
• Publication Date: 2023
• Language: English
• ISBN 10: 1108926649
• ISBN 13: 9781108926645
• Binding: Taschenbuch
• Edition: 2nd Edition
• Condition: Neu
• Dimensions: 224W x 152H x 27D millimeters 224 millimeters width by 152 millimeters height by 27 millimeters depth
• Weight: 623 grams

About this title

Synopsis

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.

About the Authors

Simo Särkkä is Associate Professor in the Department of Electrical Engineering and Automation at Aalto University, Finland. His research interests center on state estimation and stochastic modeling, and he has authored two books (2013 and 2019) on these topics. He is Fellow of ELLIS, Senior Member of IEEE, a recipient of multiple paper awards, and he has been Chair of MLSP and FUSION conferences.

Lennart Svensson is Professor in the Department of Electrical Engineering at Chalmers University of Technology, Gothenberg. His research focuses on nonlinear filtering, deep learning, and tracking in particular. He has organized a massive open online course on multiple object tracking, and received paper awards at the International Conference on Information Fusion in 2009, 2010, 2017, 2019, and 2021.