Recursive nonlinear estimation : a geometric approach /

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Bibliographic Details
Author / Creator:Kulhavý, Rudolf, 1957-
Imprint:Berlin ; New York : Springer, ©1996.
Description:1 online resource (xvi, 224 pages) : illustrations.
Language:English
Series:Lecture notes in control and information sciences ; 216
Lecture notes in control and information sciences ; 216.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11073820
Hidden Bibliographic Details
ISBN:9783540409472
3540409475
3540760636
9783540760634
Notes:Includes bibliographical references (pages 213-222) and index.
Restrictions unspecified
Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2010.
Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. http://purl.oclc.org/DLF/benchrepro0212
digitized 2010 HathiTrust Digital Library committed to preserve
Print version record.
Summary: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 an appealing Pythagorean-like geometry of the empirical and model distributions, the book brings a new 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; the former case giving initial insight into the latter case which is of primary interest to the control community. A number of examples illustrate the key concepts and tools used. This unique monograph follows some previous results on the Pythagorean theory of estimation in the literature (e.g., Chentsov, Csiszar and Amari) but extends the results to the case of controlled dynamic systems.
Other form:Print version: Kulhavý, Rudolf, 1957- Recursive nonlinear estimation. Berlin ; New York : Springer, ©1996

MARC

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504 |a Includes bibliographical references (pages 213-222) and index. 
505 0 |a 1. Inference Under Constraints -- 2. From Matching Data to Matching Probabilities -- 3. Optimal Estimation with Compressed Data -- 4. Approximate Estimation with Compressed Data -- 5. Numerical Implementation -- 6. Concluding Remarks -- A. Selected Topics from Probability Theory -- B. Selected Topics from Convex Optimization. 
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520 |a 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 an appealing Pythagorean-like geometry of the empirical and model distributions, the book brings a new 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; the former case giving initial insight into the latter case which is of primary interest to the control community. A number of examples illustrate the key concepts and tools used. This unique monograph follows some previous results on the Pythagorean theory of estimation in the literature (e.g., Chentsov, Csiszar and Amari) but extends the results to the case of controlled dynamic systems. 
588 0 |a Print version record. 
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650 4 |a théorie estimation. 
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650 7 |a Systèmes, identification des.  |2 ram 
650 7 |a Statistique bayésienne.  |2 ram 
650 0 7 |a Nichtlineare Schätzung.  |2 swd 
650 0 7 |a Parameterschätzung.  |2 swd 
650 0 7 |a Rekursive Parameterschätzung.  |2 swd 
655 4 |a Electronic books. 
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