This book represents a new and modern approach to presenting the foundations of statistical algorithms. It differs from other books on the market for at least three reasons:

(i)

it covers the historical development and clarifies the evolution of more powerful algorithms,

(ii)

it emphasizes certain recurring themes in all statistical algorithms, such as computation, assessment and verification, iteration, intuition, randomness, repetition scalability, and parallelization,

(iii)

it covers two topics neglected in other books, namely, systematic verification and scalability.

Each chapter offers examples, exercises and solutions to the selected exercises. It also provides access to a website with supplementary material: program code for selected figures, simulations, and exercises. Most exercises are considered to be solved using R.
The book is suitable for readers who not only want to understand current statistical algorithms, but also gain a deeper understanding of how the algorithms are constructed and how they operate. It is addressed first and foremost to students and lecturers teaching the foundations of statistical algorithms.