A new and refreshingly different approach to presenting the foundations
of statistical algorithms, Foundations of Statistical Algorithms:
With References to R Packages reviews the historical development of
basic algorithms to illuminate the evolution of today's more powerful
statistical algorithms. It emphasizes recurring themes in all
statistical algorithms, including computation, assessment and
verification, iteration, intuition, randomness, repetition and
parallelization, and scalability. Unique in scope, the book reviews the
upcoming challenge of scaling many of the established techniques to very
large data sets and delves into systematic verification by demonstrating
how to derive general classes of worst case inputs and emphasizing the
importance of testing over a large number of different inputs.
Broadly accessible, the book offers examples, exercises, and selected
solutions in each chapter as well as access to a supplementary website.
After working through the material covered in the book, readers should
not only understand current algorithms but also gain a deeper
understanding of how algorithms are constructed, how to evaluate new
algorithms, which recurring principles are used to tackle some of the
tough problems statistical programmers face, and how to take an idea for
a new method and turn it into something practically useful.