Data Analysis for Scientists and Engineers
Enregistré dans:
| Auteur principal: | |
|---|---|
| Support: | E-Book |
| Langue: | Anglais |
| Publié: |
Princeton, NJ :
Princeton University Press,
[2017].
Princeton ; Oxford : Princeton university press, 2016. |
| Édition: | Core Textbook. |
| Sujets: | |
| Autres localisations: | Voir dans le Sudoc |
| Résumé: | Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. In-depth discussion of data analysis for scientists and engineers Coverage of both frequentist and Bayesian approaches to data analysis Extensive look at analysis techniques for time-series data and images Detailed exploration of linear and nonlinear modeling of data Emphasis on error analysis Instructor's manual (available only to professors) |
| Accès en ligne: | Accès à l'E-book |
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| 245 | 1 | 0 | |a Data Analysis for Scientists and Engineers |c Edward L. Robinson. |
| 250 | |a Core Textbook. | ||
| 256 | |a Données textuelles. | ||
| 264 | 1 | |a Princeton, NJ : |b Princeton University Press, |c [2017]. | |
| 264 | 1 | |a Princeton ; |a Oxford : |b Princeton university press, |c 2016. | |
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| 500 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed Feb. 24, 2017) | ||
| 500 | |a La pagination de l'édition imprimée correspondante est de 409 p. | ||
| 506 | |a L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition | ||
| 520 | |a Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. In-depth discussion of data analysis for scientists and engineers Coverage of both frequentist and Bayesian approaches to data analysis Extensive look at analysis techniques for time-series data and images Detailed exploration of linear and nonlinear modeling of data Emphasis on error analysis Instructor's manual (available only to professors) | ||
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