Modeling with Data: Tools and Techniques for Scientific Computing

✍ Scribed by Ben Klemens

• Publisher: Princeton University Press
• Year: 2008
• Tongue: English
• Leaves: 470
• Edition: Course Book
• Category: Library

✦ 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.

✦ Table of Contents

Contents
Preface
Chapter 1. Statistics in the modern day
Part I. Computing
Chapter 2. C
Chapter 3. Databases
Chapter 4. Matrices and models
Chapter 5. Graphics
Chapter 6. More coding tools
Part II. Statistics
Chapter 7. Distributions for description
Chapter 8. Linear projections
Chapter 9. Hypothesis testing with the CLT
Chapter 10. Maximum likelihood estimation
Chapter 11. Monte Carlo
Appendix A: Environments and makefiles
Appendix B: Text processing
Appendix C: Glossary
Bibliography
Index