The interface between the computer and statistical sciences is a rapidly-developing field of research, as each discipline seeks to harness the power and resources of the other. This series aims to capture new developments and summarize what is known over the whole spectrum of computer science and statistics. It seeks to foster the integration of computer science and statistical, numerical and probabilistic methods by publishing a broad range of reference works, textbooks and handbooks.
The scope of the series is wide, including data mining, machine learning, AI, computational stats, exploratory data analysis, pattern recognition, learning theory, statistical software and graphics, graphical models, Bayesian data analysis, and internet data analysis. The titles included in the series are designed to appeal to students, researchers and professionals in computer and information science, statistics, mathematics, and engineering, as well as interdisciplinary researchers across many scientific disciplines. The inclusion of real examples and applications is highly encouraged, as is specific software.
Please contact us if you have an idea for a book for the series.
By Paul Murrell
May 31, 2017
Providing key information on how to work with research data, Introduction to Data Technologies presents ideas and techniques for performing critical, behind-the-scenes tasks that take up so much time and effort yet typically receive little attention in formal education. With a focus on ...
By Francois Husson, Sebastien Le, Jérôme Pagès
April 25, 2017
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when ...
Edited
By Claus Weihs, Dietmar Jannach, Igor Vatolkin, Guenter Rudolph
November 07, 2016
This book provides a comprehensive overview of music data analysis, from introductory material to advanced concepts. It covers various applications including transcription and segmentation as well as chord and harmony, instrument and tempo recognition. It also discusses the implementation aspects ...
By Wendy L. Martinez, Angel R. Martinez
December 22, 2015
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 ...
By MoonJung Cho, Wendy L. Martinez
December 15, 2014
Fulfilling the need for a practical user’s guide, Statistics in MATLAB: A Primer provides an accessible introduction to the latest version of MATLAB® and its extensive functionality for statistics. Assuming a basic knowledge of statistics and probability as well as a fundamental understanding of ...
Edited
By Jorg Blasius, Michael Greenacre
April 10, 2014
Visualization and Verbalization of Data shows how correspondence analysis and related techniques enable the display of data in graphical form, which results in the verbalization of the structures in data. Renowned researchers in the field trace the history of these techniques and cover their ...
By Kevin B. Korb, Ann E. Nicholson
December 16, 2010
Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal ...
By Robert Gentleman
July 14, 2008
Due to its data handling and modeling capabilities as well as its flexibility, R is becoming the most widely used software in bioinformatics. R Programming for Bioinformatics explores the programming skills needed to use this software tool for the solution of bioinformatics and computational ...
By Kai-Tai Fang, Runze Li, Agus Sudjianto
October 14, 2005
Computer simulations based on mathematical models have become ubiquitous across the engineering disciplines and throughout the physical sciences. Successful use of a simulation model, however, requires careful interrogation of the model through systematic computer experiments. While specific ...