"Classic Problems of Probability" is the winner of the 2012 PROSE Award for Mathematics from The American Publishers Awards for Professional and Scholarly Excellence.
"A great book, one that I will certainly add to my personal library." --Paul J. Nahin, Professor Emeritus of Electrical...
As the world becomes increasingly complex, so do the statistical models required to analyse the challenging problems ahead. For the very first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and application of ABC methods. These simple, but...
A classic -- offering comprehensive and unified coverage with a balance between theory and practice!
Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition and audio classification, communications, computer-aided diagnosis, data mining. The authors,...
My thanks are due to the many people who have assisted in the work reported here and in the preparation of this book. The work is incomplete and this account of it rougher than it might be. Such virtues as it has owe much to others; the faults are all mine.
My work leading to this book began when David Boulton and I attempted to develop...
This book functions as the definitive overview of the field of super-resolution imaging. Written by the leading researchers in the field of image and video super-resolution, it surveys the latest state-of-the-art techniques in super-resolution imaging. Each detailed chapter provides coverage of the implementations and applications of...
Machine Learning:Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two...
Relevant to, and drawing from, a range of disciplines, the chapters in this collection show the diversity, and applicability, of research in Bayesian argumentation. Together, they form a challenge to philosophers versed in both the use and criticism of Bayesian models who have largely overlooked their potential in argumentation. Selected from...
Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing...
This volume presents in detail the fundamental theories of linear regression analysis and diagnosis, as well as the relevant statistical computing techniques so that readers are able to actually model the data using the methods and techniques described in the book. It covers the fundamental theories in linear regression analysis and is...
Emphasizing the impact of computer software and computational technology on econometric theory and development, this reference presents advances in the application of computerized tools to econometric techniques and practices. The book focuses on current innovations in Monte Carlo simulation, computer-aided testing, and Bayesian methodology for...
This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its...