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Modeling with Data: Tools and Techniques for Scientific Computing

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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 )

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.

About the Author

Ben Klemens is a senior statistician at the National Institute of Mental Health. He is also a guest scholar at the Center on Social and Economic Dynamics at the Brookings Institution.
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