A termék adatai:

ISBN13:9780323917230
ISBN10:0323917232
Kötéstípus:Puhakötés
Terjedelem:800 oldal
Méret:234x191 mm
Nyelv:angol
Illusztrációk: 300 illustrations (100 in full color)
700
Témakör:

Data Analysis Methods in Physical Oceanography

Fourth and Revised Edition
 
Kiadás sorszáma: 4
Kiadó: Elsevier Science
Megjelenés dátuma:
 
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Kiadói listaár:
EUR 51.95
Becsült forint ár:
21 437 Ft (20 416 Ft + 5% áfa)
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17 149 (16 333 Ft + 5% áfa )
Kedvezmény(ek): 20% (kb. 4 287 Ft)
A kedvezmény érvényes eddig: 2024. június 30.
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Hosszú leírás:

Data Analysis Methods in Physical Oceanography: Fourth Edition provides a practical reference to established and modern data analysis techniques in earth and ocean sciences. In five sections, the book addresses data acquisition and recording, data processing and presentation, statistical methods and error handling, analysis of spatial data fields, and time series analysis methods. The updated edition includes new information on autonomous platforms and new analysis tools such as "deep learning” and convolutional neural networks. A section on extreme value statistics has been added, and the section on wavelet analysis has been expanded. This book brings together relevant techniques and references recent papers where these techniques have been trialed. In addition, it presents valuable examples using physical oceanography data. For students, the sections on data acquisition are useful for a compilation of all the measurement methods.




  • Includes content co-authored by scientists from academia and industry, both of whom have more than 30 years of experience in oceanographic research and field work
  • Provides boxed worked examples that address typical data analysis problems, including examples with computer code (e.g., python code, MATLAB code)
  • Presents brief summaries at the end of the more difficult sections to help readers looking for foundational information
Tartalomjegyzék:
1. Data Acquisition and Recording
2. Data Processing and Presentation
3. Statistical Methods and Error Handling
4. The Spatial Analyses of Data Fields
5. Time-series Analysis Methods
6. Digital Filters
7. Machine Learning Methods
8. Neural networks, convolutional neural networks and deep learning