Modeling with Data: Tools and Techniques for Scientific Computing

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

**Tag(s):**
Mathematics
Statistics

**Publication date**: 24 Mar 2009

**ISBN-10**:
069113314X

**ISBN-13**:
9780691133140

**Paperback**:
472 pages

**Views**: 5,735

Modeling with Data: Tools and Techniques for Scientific Computing

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

From the Introduction:

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.

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.

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About The Author(s)

Ben Klemens has worked on computational modeling projects in a surprising range of contexts, including work on cross-border migration at the World Bank, terrorist networks and voter turnout with the Brookings Institution, the genetic roots of mental disorders with the National Institute of Mental Health, network externalities as a grad student at Caltech, and even flogging in the 19th-century U.S. Navy at NYU.

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