Modeling with Data: Tools and Techniques for Scientific Computing

Ben Klemens

ISBN 10: 069113314X / ISBN 13: 9780691133140

Editore: Princeton University Press, 2008

Riguardo questo articolo

Explains how to execute computationally intensive analysis on very large data sets. This book shows readers how to determine some of the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Num Pages: 472 pages, 35 line illus. 16 tables. BIC Classification: GPH; UFM; UMT; UNA. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 267 x 186 x 31. Weight in Grams: 1138. . 2008. Hardcover. . . . . Books ship from the US and Ireland. Codice articolo V9780691133140

Dati bibliografici

Titolo: Modeling with Data: Tools and Techniques for...

Casa editrice: Princeton University Press

Data di pubblicazione: 2008

Legatura: Rilegato

Condizione: New

Descrizione articolo

Riassunto:

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. He then demonstrates how to easily apply these tools to the many threads of statistical technique, including classical, Bayesian, maximum likelihood, and Monte Carlo methods. Klemens's accessible survey describes these models in a unified and nontraditional manner, providing alternative ways of looking at statistical concepts that often befuddle students. The book includes nearly one hundred sample programs of all kinds. Links to these programs will be available on this page at a later date.

Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.

Recensione:

"This book presents an original, cheap and powerful solution to the problem of analysis of large data sets. . . . The book is devoted mainly to the practitioner of Statistics, but is also useful to mathematicians, computer scientists, researchers and students in the biology, economics and social sciences." --Radu Trimbitas, StudiaUBB