Modeling with Data : Tools and Techniques for Scientific Computing.
Title:
Modeling with Data : Tools and Techniques for Scientific Computing.
Author:
Klemens, Ben.
ISBN:
9781400828746
Personal Author:
Physical Description:
1 online resource (471 pages)
Contents:
Cover -- Contents -- Preface -- Chapter 1. Statistics in the modern day -- PART I: COMPUTING -- Chapter 2. C -- 2.1 Lines -- 2.2 Variables and their declarations -- 2.3 Functions -- 2.4 The debugger -- 2.5 Compiling and running -- 2.6 Pointers -- 2.7 Arrays and other pointer tricks -- 2.8 Strings -- 2.9 (Omitted) Errors -- Chapter 3. Databases -- 3.1 Basic queries -- 3.2 (Omitted) Doing more with queries -- 3.3 Joins and subqueries -- 3.4 On database design -- 3.5 Folding queries into C code -- 3.6 Maddening details -- 3.7 Some examples -- Chapter 4. Matrices and models -- 4.1 The GSL's matrices and vectors -- 4.2 apop_data -- 4.3 Shunting data -- 4.4 Linear algebra -- 4.5 Numbers -- 4.6 (Omitted) gsl_matrix and gsl_vector internals -- 4.7 Models -- Chapter 5. Graphics -- 5.1 plot -- 5.2 (Omitted) Some common settings -- 5.3 From arrays to plots -- 5.4 A sampling of special plots -- 5.5 Animation -- 5.6 On producing good plots -- 5.7 (Omitted) Graphs-nodes and flowcharts -- 5.8 (Omitted) Printing and LATEX -- Chapter 6. (Omitted) More coding tools -- 6.1 Function pointers -- 6.2 Data structures -- 6.3 Parameters -- 6.4 (Omitted) Syntactic sugar -- 6.5 More tools -- PART II: STATISTICS -- Chapter 7. Distributions for description -- 7.1 Moments -- 7.2 Sample distributions -- 7.3 Using the sample distributions -- 7.4 Non-parametric description -- Chapter 8. Linear projections -- 8.1 (Omitted) Principal component analysis -- 8.2 OLS and friends -- 8.3 Discrete variables -- 8.4 Multilevel modeling -- Chapter 9. Hypothesis testing with the CLT -- 9.1 The Central Limit Theorem -- 9.2 Meet the Gaussian family -- 9.3 Testing a hypothesis -- 9.4 ANOVA -- 9.5 Regression -- 9.6 Goodness of fit -- Chapter 10. Maximum likelihood estimation -- 10.1 Log likelihood and friends -- 10.2 Description: Maximum likelihood estimators -- 10.3 Missing data.
10.4 Testing with likelihoods -- Chapter 11. Monte Carlo -- 11.1 Random number generation -- 11.2 Description: Finding statistics for a distribution -- 11.3 Inference: Finding statistics for a parameter -- 11.4 Drawing a distribution -- 11.5 Non-parametric testing -- Appendix A: Environments and makefiles -- A.1 Environment variables -- A.2 Paths -- A.3 Make -- Appendix B: Text processing -- B.1 Shell scripts -- B.2 Some tools for scripting -- B.3 Regular expressions -- B.4 Adding and deleting -- B.5 More examples -- Appendix C: Glossary -- Bibliography -- Index -- A -- B -- C -- D -- E -- F -- G -- H -- I -- J -- K -- L -- M -- N -- O -- P -- Q -- R -- S -- T -- U -- V -- W -- X -- Y -- Z.
Abstract:
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.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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