By Saerkkae S.

Filtering and smoothing tools are used to supply a correct estimate of the kingdom of a time-varying procedure according to a number of observational inputs (data). curiosity in those tools has exploded lately, with a number of purposes rising in fields reminiscent of navigation, aerospace engineering, telecommunications and drugs. This compact, casual creation for graduate scholars and complicated undergraduates provides the present state of the art filtering and smoothing tools in a unified Bayesian framework. Readers examine what non-linear Kalman filters and particle filters are, how they're comparable, and their relative merits and downsides. additionally they notice how cutting-edge Bayesian parameter estimation tools should be mixed with cutting-edge filtering and smoothing algorithms. The book's useful and algorithmic process assumes basically modest mathematical must haves. Examples comprise MATLAB computations, and the various end-of-chapter workouts contain computational assignments. MATLAB/GNU Octave resource code is offered for obtain at www.cambridge.org/sarkka, selling hands-on paintings with the tools

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Extra resources for Bayesian Filtering and Smoothing

Example text

The unscented Kalman filter (UKF) approximates the propagation of densities through the non-linearities of measurement and noise processes using the unscented transform. This also results in a Gaussian approximation. The unscented Rauch–Tung–Striebel smoother (URTSS) is the approximate non-linear smoothing algorithm corresponding to UKF. Sequential Monte Carlo methods or particle filters and smoothers represent the posterior distribution as a weighted set of Monte Carlo samples. 14 What are Bayesian filtering and smoothing?

4. 3 Batch and recursive Bayesian estimation In order to understand the meaning and applicability of Bayesian filtering and its relationship to recursive estimation, it is useful to go through an example where we solve a simple and familiar linear regression problem in a recursive manner. After that we generalize this concept to include a dynamic model in order to illustrate the differences in dynamic and batch estimation. m0 ; P0 /. tT ; yT /g. 1. 1 tk / and N. 1). Note that we denote the row vector Hk in matrix notation, because it generally is a matrix (when the measurements are vector valued) and we want to avoid using different notations for scalar and vector measurements.

Use a suitable Gaussian distribution as the importance distribution for the parameters Â. 4. 3 Batch and recursive Bayesian estimation In order to understand the meaning and applicability of Bayesian filtering and its relationship to recursive estimation, it is useful to go through an example where we solve a simple and familiar linear regression problem in a recursive manner. After that we generalize this concept to include a dynamic model in order to illustrate the differences in dynamic and batch estimation.