A Strong Practical Focus on Applications and AlgorithmsComputational Statistics Handbook with MATLAB, Third Edition covers today's most commonly used techniques in computational statistics while maintaining the same philosophy and writing style of the bestselling previous editions. The text keeps theoretical concepts to a minimum, emphasizing the i

Approaching computational statistics through its theoretical aspects can be daunting. Often intimidated or distracted by the theory, researchers and students can lose sight of the actual goals and applications of the subject. What they need are its key concepts, an understanding of its methods, experience with its implementation, and practice with

Exploratory data analysis (EDA) was conceived at a time when computers were not widely used, and thus computational ability was rather limited. As computational sophistication has increased, EDA has become an even more powerful process for visualizing and summarizing data before making model assumptions to generate hypotheses, encompassing larger a

This new edition of a bestseller continues the tone of the previous two, covering some of the most commonly used contemporary techniques in computational statistics. With a strong, practical focus on implementing the methods, the authors include algorithmic descriptions of the procedures as well as examples that illustrate the use of algorithms in data analysis. Written in a way that emphasizes applications and algorithms instead of theory, the authors include a no-cost toolbox that implements most of the methodologies described in the book.

Every 3rd issue is a quarterly cumulation.

This title describes the main problems in bioinformatics and explains the fundamental concepts and algorithms of machine learning. Illustrative examples from bioinformatics demonstrate the capabilities of state-of-the-art machine learning techniques and how they can be applied to bioinformatics problems.