Modeling with Data: Tools and Techniques for Scientific Computing

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Product Description

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.

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Modeling with Data: Tools and Techniques for Scientific Computing Review

The book introduces the Apophenia library, a very good statistics library for the C language. The library features a data object and a bunch of models that take the data object as an input and produce other data objects as outputs. There is a good integration with SQLite3.

The book itself is more of an introduction to statistical techniques, covering all the basics very well.

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