Modeling with Data: Tools and Techniques for Scientific Computing

Ben Klemens

ISBN 10: 069113314X / ISBN 13: 9780691133140

Published by Princeton University Press, 2008

About this Item

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. Seller Inventory # V9780691133140

Bibliographic Details

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

Publisher: Princeton University Press

Publication Date: 2008

Binding: Hardcover

Condition: New

About this title

Synopsis:

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.

From the Back Cover:

"I am a psychiatric geneticist but my degree is in neuroscience, which means that I now do far more statistics than I have been trained for. I cannot overstate to you the magnitude of the change in my productivity since finding this book. Even after reading the first few chapters, which explain why data analysis is painful and how one can implement a long-term solution, my research moved forward greatly."--Amber Baum, National Institute of Mental Health

"I enjoyed reading this book and learned a great deal from it. Modeling with Data filled in a lot of holes in my knowledge, and I think that will be true in general for other readers as well. There is a lot of high-quality and interesting material here."--Brendan Halpin, University of Limerick