Modeling with Data : : Tools and Techniques for Scientific Computing / / Ben Klemens.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Klemens, Ben,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2008]; ©2009
• Year of Publication: 2008
• Edition: Course Book
• Language: English
• Physical Description: 1 online resource (472 p.) :; 35 line illus. 16 tables.

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

Frontmatter -- 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