Bayesian Filtering and Smoothing (Institute of Mathematical Statistics Textbooks Book 3) 1st Edition by Simo Särkkä (PDF)

Description

Filtering and smoothing methods are used to produce an accurate estimate of the state of a time-varying system based on multiple observational inputs (data). Interest in these methods has exploded in recent years, with numerous applications emerging in fields such as navigation, aerospace engineering, telecommunications and medicine. This compact, informal introduction for graduate students and advanced undergraduates presents the current state-of-the-art filtering and smoothing methods in a unified Bayesian framework. Readers learn what non-linear Kalman filters and particle filters are, how they are related, and their relative advantages and disadvantages. They also discover how state-of-the-art Bayesian parameter estimation methods can be combined with state-of-the-art filtering and smoothing algorithms. The book’s practical and algorithmic approach assumes only modest mathematical prerequisites. Examples include Matlab computations, and the numerous end-of-chapter exercises include computational assignments. Matlab code is available for download at www.cambridge.org/sarkka, promoting hands-on work with the methods.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I bought this because it is one of the few books I’ve seen that spells out the extended RTS filter. Nice explanation. This author has some lecture notes up on the web, and they go nicely with this book. The price is great. The only downside I found is that it could use a few COMPLETE examples. Most are set up and then graphical results given. I’d like to see a few more intermediate steps or results. Like my title says, the notation is a little different than I’ve seen in other books, but that shouldn’t slow you down too much.

⭐Very comprehensive. It is a very updated book, covering most of the main methods used today. It does not go into the details, it’s more like a reference book.

⭐The book is nicely written and easy to follow! It is very suitable for the those who want to learn the basics of Bayesian inference.

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