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Dynamic data assimilation : a least squares approach / John M. Lewis, S. Lakshmivarahan, Sudarshan Dhall.

By: Contributor(s): Material type: TextTextSeries: Encyclopedia of mathematics and its applications ; 104.Publisher: Cambridge : Cambridge University Press, 2006Description: 1 online resource (xxii, 654 pages) : illustrations, mapContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781461941507 (electronic bk.)
  • 1461941504 (electronic bk.)
Subject(s): Genre/Form: Additional physical formats: Print version:: Dynamic data assimilationDDC classification:
  • 511.8 22
LOC classification:
  • QA401 .L475 2006eb
Other classification:
  • 31.70
Online resources:
Contents:
1. Synopsis -- 2. Pathways into data assimilation : illustrative examples -- 3. Applications -- 4. Brief history of data assimilation -- 5. Linear least squares estimation : method of normal equations -- 6. A geometric view : projection and invariance -- 7. Nonlinear least squares estimation -- 8. Recursive least squares estimation -- 9. Matrix methods -- 10. Optimization : steepest descent method -- 11. Conjugate direction/gradient methods -- 12. Newton and quasi-Newton methods -- 13. Principles of statistical estimation -- 14. Statistical least squares estimation -- 15. Maximum likelihood method -- 16. Bayesian estimation method -- 17. From Gauss to Kalman : sequential, linear minimum variance estimation
Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
eBook eBook e-Library EBSCO Mathematics Available
Total holds: 0

Includes bibliographical references and index.

1. Synopsis -- 2. Pathways into data assimilation : illustrative examples -- 3. Applications -- 4. Brief history of data assimilation -- 5. Linear least squares estimation : method of normal equations -- 6. A geometric view : projection and invariance -- 7. Nonlinear least squares estimation -- 8. Recursive least squares estimation -- 9. Matrix methods -- 10. Optimization : steepest descent method -- 11. Conjugate direction/gradient methods -- 12. Newton and quasi-Newton methods -- 13. Principles of statistical estimation -- 14. Statistical least squares estimation -- 15. Maximum likelihood method -- 16. Bayesian estimation method -- 17. From Gauss to Kalman : sequential, linear minimum variance estimation

Description based on print version record.

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