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.
Time Series Clustering and Classification
Textual Data Science with R
Bayesian Regression Modeling with INLA
Chain Event Graphs
Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics
Exploratory Data Analysis with MATLAB
Introduction to Data Technologies
Computational Statistics Handbook with MATLAB
Elizabeth Ann Maharaj, Pierpaolo D'Urso, Jorge Caiado
April 12, 2019
The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and...
March 01, 2019
Textual Statistics with R comprehensively covers the main multidimensional methods in textual statistics supported by a specially-written package in R. Methods discussed include correspondence analysis, clustering, and multiple factor analysis for contigency tables. Each method is illuminated by...
Brigitte Le Roux, Solène Bienaise, Jean-Luc Durand
February 22, 2019
Geometric Data Analysis designates the approach of Multivariate Statistics that conceptualizes the set of observations as a Euclidean cloud of points. Combinatorial Inference in Geometric Data Analysis gives an overview of multidimensional statistical inference methods applicable to clouds of...
Xiaofeng Wang, Yu Yue Ryan, Julian J. Faraway
February 16, 2018
INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting a broad class of Bayesian regression models. No samples of the posterior marginal distributions need to be drawn using INLA, so it is a computationally convenient alternative to Markov chain Monte Carlo (...
Rodrigo A. Collazo, Christiane Goergen, Jim Q. Smith
January 30, 2018
Written by some major contributors to the development of this class of graphical models, Chain Event Graphs introduces a viable and straightforward new tool for statistical inference, model selection and learning techniques. The book extends established technologies used in the study of...
September 07, 2017
"Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of…quite different from most data analytics texts which largely ignore foundational concepts and simply present a cookbook of methods…a very useful text and I would certainly use it in...
Wendy L. Martinez, Angel R. Martinez, Jeffrey Solka
July 27, 2017
Praise for the Second Edition:"The authors present an intuitive and easy-to-read book. … accompanied by many examples, proposed exercises, good references, and comprehensive appendices that initiate the reader unfamiliar with MATLAB."—Adolfo Alvarez Pinto, International Statistical Review "...
Karl Fraser, Zidong Wang, Xiaohui Liu
June 14, 2017
To harness the high-throughput potential of DNA microarray technology, it is crucial that the analysis stages of the process are decoupled from the requirements of operator assistance. Microarray Image Analysis: An Algorithmic Approach presents an automatic system for microarray image processing to...
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...
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...
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...
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...