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.

這本易於理解的書籍的第二版，提供了一個統一的貝葉斯方法，用於非線性狀態空間模型的最新濾波、平滑和參數估計算法。該書專注於離散時間狀態空間模型，並仔細介紹了與最優濾波和平滑相關的基本概念。特別是，它涵蓋了一系列高效的非線性高斯濾波和平滑算法，以及基於蒙特卡羅的算法。這個更新的版本新增了關於構建實際系統的狀態空間模型、離散化連續時間狀態空間模型、通過啟用近似值的高斯濾波、後驗線性化濾波和相應的平滑器的章節。擴展了關鍵主題的涵蓋範圍，包括擴展卡爾曼濾波和平滑，以及參數估計。這本書的實用、算法導向的方法僅需要一些基本的數學先備知識，適合研究生和高年級本科生。書中包含了許多例子，並提供了在線上提供的Matlab和Python代碼，讓讀者能夠在自己的項目中實施算法。