Preface
This book covers most topics needed to develop a broad and thorough working knowledge of modern computational statistics. We seek to develop a practical understanding of how and why existing methods work, enabling readers to use modern statistical methods effectively. Since many new methods are built from components of existing techniques, our ultimate goal is to provide scientists with the tools they need to contribute new ideas to the field.
A growing challenge in science is that there is so much of it. While the pursuit of important new methods and the honing of existing approaches is a worthy goal, there is also a need to organize and distill the teeming jungle of ideas. We attempt to do that here. Our choice of topics reflects our view of what constitutes the core of the evolving field of computational statistics, and what will be interesting and useful for our readers.
Our use of the adjective modern in the first sentence of this preface is potentially troublesome: There is no way that this book can cover all the latest, greatest techniques. We have not even tried. We have instead aimed to provide a reasonably up-to-date survey of a broad portion of the field, while leaving room for diversions and esoterica.
The foundations of optimization and numerical integration are covered in this book. We include these venerable topics because (i) they are cornerstones of frequentist and Bayesian inference; (ii) routine application of available software often fails for hard ...
