The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics.

The essential text and reference for modern scientific computing now also covers computational geometry, classification and inference, and much more.

An introduction to numerical analysis combining rigour with practical applications, and providing numerous exercises plus solutions.

Hints and solutions are provided to many of the exercises and problems. This work may be used as the text for a one-semester graduate or advanced undergraduate course, as well as by students engaged in self-study.

This second edition expands and updates the coverage of the first edition (1996) and includes numerous improvements to the original material.

Assuming no knowledge of programming, this book presents both programming concepts and MATLAB's built-in functions, providing a perfect platform for exploiting MATLAB's extensive capabilities for tackling engineering problems.

This edition of the standard introductory textbook on numerical analysis has been revised and updated to include optimization, trigonometric interpolation and the fast Fourier transform, numerical differentiation, the method of lines and ...

This book introduces students with diverse backgrounds to various types of mathematical analysis that are commonly needed in scientific computing.

Designed for the many applied mathematicians and engineers who wish to explore computerized numerical methods, this text communicates an enthusiasm for the power of C++, an object-oriented language, as a tool for this kind of work.

This new edition includes thoroughly revised chapters on matrix multiplication problems and parallel matrix computations, expanded treatment of CS decomposition, an updated overview of floating point arithmetic, a more accurate rendition of ...