Recursive Nonlinear Estimation: A Geometric Approach
Author:   Rudolph Kulhavy
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Edition:   1996 ed.
Volume:   216
ISBN:  

9783540760634


Pages:   227
Publication Date:   25 June 1996
Format:   Paperback
Availability:   In Print   Availability explained
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Recursive Nonlinear Estimation: A Geometric Approach


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Overview

In a close analogy to matching data in Euclidean space, this monograph views parameter estimation as matching of the empirical distribution of data with a model-based distribution. Using a Pythagorean-like geometry of the empirical and model distributions, the book suggests a solution to the problem of recursive estimation of non-Gaussian and nonlinear models which can be regarded as a specific approximation of Bayesian estimation. The cases of independent observations and controlled dynamic systems are considered in parallel; orm er case gives insight into the latter case, which should be of interest to the control community. A number of examples illustrate the key concepts and tools used.

Full Product Details

Author:   Rudolph Kulhavy
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   1996 ed.
Volume:   216
Dimensions:   Width: 15.50cm , Height: 1.30cm , Length: 23.50cm
Weight:   0.390kg
ISBN:  

9783540760634


ISBN 10:   3540760636
Pages:   227
Publication Date:   25 June 1996
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Inference under constraints.- From matching data to matching probabilities.- Optimal estimation with compressed data.- Approximate estimation with compressed data.- Numerical implementation.- Concluding remarks.- Selected topics from probability theory.- Selected topics from convex optimization.

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