James E. Gentle Libri

The fourth installment in James Gentle's series on statistical computing delves deeper into advanced methodologies and applications in the field. It builds upon previous volumes, offering comprehensive insights and practical examples that cater to both beginners and experienced practitioners. The book emphasizes innovative techniques and their implementation, making it a valuable resource for anyone looking to enhance their understanding of statistical computing.

Focusing on the theory of matrix algebra, this book delves into its applications in statistics and numerical linear algebra. It highlights various matrix types crucial for statistical analysis, emphasizing the importance of matrix algebra in data science and statistical theory. Previous editions have been updated to provide comprehensive coverage of essential mathematical topics, making it a vital resource for understanding the role of matrices in statistical applications.

This book explores computational inference, integrating it with traditional statistical methods. It covers computationally-intensive techniques, statistical computing, and numerical analysis, emphasizing algorithms and methods like Monte Carlo and bootstrap. Designed for readers with an intermediate background, it includes numerous exercises for practice.

The Handbook of Computational Statistics: Concepts and Methodology is divided into four parts. It begins with an overview over the field of Computational Statistics. The second part presents several topics in the supporting field of statistical computing. Emphasis is placed on the need of fast and accurate numerical algorithms and it discusses some of the basic methodologies for transformation, data base handling and graphics treatment. The third part focuses on statistical methodology. Special attention is given to smoothing, iterative procedures, simulation and visualization of multivariate data. Finally a set of selected applications like Bioinformatics, Medical Imaging, Finance and Network Intrusion Detection highlight the usefulness of computational statistics.

Accurate and efficient computer algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors. Regardless of the software system used, the book describes and gives examples of the use of modern computer software for numerical linear algebra. It begins with a discussion of the basics of numerical computations, and then describes the relevant properties of matrix inverses, factorisations, matrix and vector norms, and other topics in linear algebra. The book is essentially self- contained, with the topics addressed constituting the essential material for an introductory course in statistical computing. Numerous exercises allow the text to be used for a first course in statistical computing or as supplementary text for various courses that emphasise computations.

Will provide a more elementary introduction to these topics than other books available; Gentle is the author of two other Springer books

This book surveys techniques of random number generation and the use of random numbers in Monte Carlo simulation. The book covers basic principles, as well as newer methods such as parallel random number generation, nonlinear congruential generators, quasi Monte Carlo methods, and Markov chain Monte Carlo. The best methods for generating random varieties from the standard distributions are presented, but also general techniques useful in more complicated models and in novel settings are described. The emphasis throughout the book is on practical methods that work well in current computing environments. The book includes exercises and can be used as the primary text for a specialized course in statistical computing, or as a supplementary text for a course in computational statistics and other areas of modern statistics that rely on simulation. The book, which covers recent developments in the field, is also a useful reference for practitioners.

Computer simulation has emerged as a vital tool in scientific research, complementing traditional methods like experimentation and theoretical analysis. This book explores the methodologies and applications of simulation across various fields, highlighting its role in enhancing understanding and predicting complex systems. It delves into the intricacies of designing simulations, analyzing data, and interpreting results, making it an essential resource for researchers and practitioners looking to leverage technology in their work.