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  E-mail Message: I thought you might be interested in this item at http://www.worldcat.org/oclc/944469135 Title: Data analysis for scientists and engineers Author: Edward L Robinson, (Professor of astronomy) Publisher: Princeton : Princeton University Press, [2016] ©2016 ISBN/ISSN: 9780691169927 0691169926 OCLC:944469135

  
  

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Data analysis for scientists and engineers

Author:	Edward L Robinson, (Professor of astronomy)
Publisher:	Princeton : Princeton University Press, [2016] ©2016
Edition/Format:	Print book : EnglishView all editions and formats
Summary:	"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."--  Read more...
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Subjects	Probabilities. Mathematical statistics. Engineering -- Data processing. View all subjects
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