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120604s2012 njua ob 001 0 eng d |
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|a 798710560
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|a 1118287797 (electronic bk.)
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|a 1118287835 (electronic bk.)
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|a 9781118287798 (electronic bk.)
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|a 9781118287835 (electronic bk.)
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|a (OCoLC)794663337
|z (OCoLC)798710560
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|a (OCoLC)ocn794663337
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|a 4004436
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|a 10.1002/9781118287798
|b Wiley InterScience
|n http://www3.interscience.wiley.com
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|a EBLCP
|b eng
|c EBLCP
|d OCLCQ
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|a QA279.5
|b .H38 2012
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|a MAT
|x 029010
|2 bisacsh
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0 |
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|a 519.5/42
|a 519.542
|
| 100 |
1 |
|
|a Haug, Anton J.,
|d 1941-
|0 http://id.loc.gov/authorities/names/n2011082992
|
| 245 |
1 |
0 |
|a Bayesian estimation and tracking :
|b a practical guide /
|c Anton J. Haug.
|
| 264 |
|
1 |
|a Hoboken :
|b John Wiley & Sons,
|c 2012.
|
| 300 |
|
|
|a 1 online resource (xxvi, 369 pages) :
|b illustrations
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
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| 500 |
|
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|a Electronic resource.
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 520 |
|
|
|a A practical approach to estimating and tracking dynamic systems in real-worl applications Much of the literature on performing estimation for non-Gaussian systems is short on practical methodology, while Gaussian methods often lack a cohesive derivation. Bayesian Estimation and Tracking addresses the gap in the field on both accounts, providing readers with a comprehensive overview of methods for estimating both linear and nonlinear dynamic systems driven by Gaussian and non-Gaussian noices. Featuring a unified approach to Bayesian estimation and tracking, the book emphasizes the derivation of all tracking algorithms within a Bayesian framework and describes effective numerical methods for evaluating density-weighted integrals, including linear and nonlinear Kalman filters for Gaussian-weighted integrals and particle filters for non-Gaussian cases. The author first emphasizes detailed derivations from first principles of eeach estimation method and goes on to use illustrative and detailed step-by-step instructions for each method that makes coding of the tracking filter simple and easy to understand.
|
| 588 |
|
|
|a Description based on print version record.
|
| 650 |
|
0 |
|a Automatic tracking
|x Mathematics.
|
| 650 |
|
0 |
|a Bayesian statistical decision theory.
|0 http://id.loc.gov/authorities/subjects/sh85012506
|
| 650 |
|
0 |
|a Estimation theory.
|0 http://id.loc.gov/authorities/subjects/sh85044957
|
| 650 |
|
4 |
|a Automatic tracking
|x Mathematics.
|
| 650 |
|
4 |
|a Bayesian statistical decision theory.
|
| 650 |
|
4 |
|a Estimation theory.
|
| 650 |
|
4 |
|a Mathematics.
|
| 650 |
|
7 |
|a MATHEMATICS / Probability & Statistics / Bayesian Analysis.
|2 bisacsh
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 776 |
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|i Print version:
|a Haug, Anton J.
|t Bayesian Estimation and Tracking : A Practical Guide
|d Hoboken : John Wiley & Sons, c2012
|z 9780470621707
|
| 830 |
|
0 |
|a Wiley Online Library.
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://dx.doi.org/10.1002/9781118287798
|z Connect to the full text of this electronic book
|t 0
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|c 2012/7/26
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|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e QA279.5 .H38 2012
|h Library of Congress classification
|
| 998 |
f |
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|a QA279.5 .H38 2012
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|l Available Online
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