Bayesian Filtering and Smoothing

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.

Product details

• Authors: Simo Sarkka, Simo (Aalto University Sarkka, Simo Särkkä, Lennart Svensson, Lennart (Chalmers University of Technology Svensson
• Publisher: Cambridge University Press Academic
• Languages: English
• Product format: Paperback / Softback
• Released: 01.05.2023
• EAN: 9781108926645
• ISBN: 978-1-108-92664-5
• No. of pages: 430
• Dimensions: 150 mm x 240 mm x 30 mm
• Series: Institute of Mathematical Statistics Textbooks; Print on Demand; Institute of Mathematical Stat
• Subjects: Education and learning > Teaching preparation > Vocational needs; Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; machine learning, MATHEMATICS / Probability & Statistics / General, TECHNOLOGY & ENGINEERING / Engineering (General), MATHEMATICS / Differential Equations / General, BUSINESS & ECONOMICS / Econometrics, Economic statistics, Probability & statistics, Differential calculus & equations, Stochastics, Engineering: general, Econometrics, Probability and statistics, COMPUTERS / Data Science / Machine Learning, Data analysis: general, Data science and analysis: general, Econometrics and economic statistics, Differential calculus and equations