| Tag |
First Indicator |
Second Indicator |
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| LEADER |
00000cam a2200000 i 4500 |
| 001 |
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| 006 |
m o d |
| 007 |
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| 008 |
130908s2014 flu ob 001 0 eng d |
| 005 |
20250728184623.2 |
| 035 |
|
|
|a (OCoLC)ocn858956107
|
| 040 |
|
|
|a YDXCP
|b eng
|e pn
|c YDXCP
|d OCLCO
|d E7B
|d STF
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|d CQ$
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|d OCLCQ
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|d OCLCQ
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| 019 |
|
|
|a 857059565
|a 988501554
|a 1065706199
|a 1110586103
|a 1260359009
|a 1330993311
|a 1498460908
|a 1503849830
|
| 020 |
|
|
|a 1584887362
|q (electronic bk.)
|
| 020 |
|
|
|a 9781584887362
|q (electronic bk.)
|
| 020 |
|
|
|z 9781584887355
|q (hardback)
|
| 020 |
|
|
|z 1584887354
|q (hardback)
|
| 020 |
|
|
|a 9780429136542
|q (e-book : PDF)
|
| 020 |
|
|
|a 0429136544
|
| 024 |
7 |
|
|a 10.1201/b15334
|2 doi
|
| 035 |
|
|
|a (OCoLC)858956107
|z (OCoLC)857059565
|z (OCoLC)988501554
|z (OCoLC)1065706199
|z (OCoLC)1110586103
|z (OCoLC)1260359009
|z (OCoLC)1330993311
|z (OCoLC)1498460908
|z (OCoLC)1503849830
|
| 050 |
|
4 |
|a QA279
|b .C33 2014
|
| 082 |
0 |
4 |
|a 519.5/38
|2 23
|
| 084 |
|
|
|a MAT003000
|2 bisacsh
|
| 049 |
|
|
|a TXAM
|
| 100 |
1 |
|
|a Cacuci, Dan Gabriel,
|e author.
|
| 245 |
1 |
0 |
|a Computational methods for data evaluation and assimilation /
|c Dan G. Cacuci, Ionel Michael Navon, Mihaela Ionescu-Bujor.
|
| 264 |
|
1 |
|a Boca Raton :
|b CRC Press,
|c 2014.
|
| 300 |
|
|
|a 1 online resource
|
| 336 |
|
|
|a text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a computer
|b c
|2 rdamedia
|
| 338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
| 520 |
|
|
|a "Data evaluation and data combination require the use of a wide range of probability theory concepts and tools, from deductive statistics mainly concerning frequencies and sample tallies to inductive inference for assimilating non-frequency data and a priori knowledge. Computational Methods for Data Evaluation and Assimilation presents interdisciplinary methods for integrating experimental and computational information. This self-contained book shows how the methods can be applied in many scientific and engineering areas. After presenting the fundamentals underlying the evaluation of experimental data, the book explains how to estimate covariances and confidence intervals from experimental data. It then describes algorithms for both unconstrained and constrained minimization of large-scale systems, such as time-dependent variational data assimilation in weather prediction and similar applications in the geophysical sciences. The book also discusses several basic principles of four-dimensional variational assimilation (4D VAR) and highlights specific difficulties in applying 4D VAR to large-scale operational numerical weather prediction models"--
|c Provided by publisher.
|
| 520 |
|
|
|a "Preface This book is addressed to graduate and postgraduate students and researchers in the interdisciplinary methods of data assimilation, which refers to the integration of experimental and computational information. Since experiments and corresponding computations are encountered in many fields of scientific and engineering endeavors, the concepts presented in this book are illustrated using paradigm examples that range from the geophysical sciences to nuclear physics. In an attempt to keep the book as self-contained as possible, the mathematical concepts mostly from probability theory and functional analysis needed to follow the material presented in the book's five chapters, are summarized in the book's three appendices. This book was finalized at the University of South Carolina. The authors wish to acknowledge the outstanding professional assistance of Dr. Madalina Corina Badea of the University of South Carolina, who has thoroughly reviewed the final version of the book, providing very valuable suggestions while improving its readability. Also acknowledged are the services of Dr. Erkan Arslan for his typing the word-version of this book into Latex. Last but not least, this book would have not have appeared without the continued patience, guidance, and understanding of Bob Stern (Executive Editor, Taylor and Francis Group), whom the authors appreciate immensely"--
|c Provided by publisher.
|
| 504 |
|
|
|a Includes bibliographical references and index.
|
| 588 |
0 |
|
|a Print version record.
|
| 505 |
0 |
|
|a 1. Experimental data evaluation : basic concepts -- 2. Computation of means and variances from measurements -- 3. Optimization methods for large-scale data assimilation -- 4. Basic principles of 4-D VAR -- 5. 4-D VAR in numerical weather prediction models -- 6. Appendix A -- 7. Appendix B -- 8. Appendix C.
|
| 546 |
|
|
|a English.
|
| 650 |
|
0 |
|a Analysis of covariance.
|
| 650 |
|
0 |
|a Analysis of means.
|
| 650 |
|
0 |
|a Multivariate analysis.
|
| 650 |
|
0 |
|a Data integration (Computer science)
|
| 650 |
|
6 |
|a Analyse de covariance.
|
| 650 |
|
6 |
|a Analyse des moyennes (Statistique mathématique)
|
| 650 |
|
6 |
|a Analyse multivariée.
|
| 650 |
|
6 |
|a Intégration de données (Informatique)
|
| 650 |
|
7 |
|a MATHEMATICS
|x Applied.
|2 bisacsh
|
| 650 |
|
7 |
|a Analysis of covariance
|2 fast
|
| 650 |
|
7 |
|a Analysis of means
|2 fast
|
| 650 |
|
7 |
|a Data integration (Computer science)
|2 fast
|
| 650 |
|
7 |
|a Multivariate analysis
|2 fast
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 655 |
|
4 |
|a Llibres electrònics.
|
| 700 |
1 |
|
|a Navon, Ionel Michael,
|e author.
|
| 700 |
1 |
|
|a Ionescu-Bujor, Mihaela,
|e author.
|
| 710 |
2 |
|
|a Taylor & Francis.
|
| 758 |
|
|
|i has work:
|a Computational methods for data evaluation and assimilation (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCFWJgGJFhjtPpGrGRVH9jC
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Cacuci, Dan Gabriel.
|t Computational methods for data evaluation and assimilation.
|z 9781584887355
|z 1584887354
|w (DLC) 2013026450
|
| 776 |
1 |
|
|z 1-58488-736-2
|
| 776 |
0 |
8 |
|c Original
|z 9781584887355
|z 1584887354
|w (DLC) 2013026450
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://www.taylorfrancis.com/books/9780429136542
|z Connect to the full text of this electronic book
|t 0
|
| 936 |
|
|
|a BATCHLOAD
|
| 955 |
|
|
|a Taylor and Francis MATHnetBASE
|
| 994 |
|
|
|a 92
|b TXA
|
| 999 |
f |
f |
|s 09c98be5-accf-46bb-9d65-23734e105eba
|i f5249530-b026-4fa9-9e77-934e850f8599
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e QA279 .C33 2014
|h Library of Congress classification
|
| 998 |
f |
f |
|a QA279 .C33 2014
|t 0
|l Available Online
|