Reproducing Kernel Methods for Machine Learning, PDEs, and Statistics

Reproducing Kernel Methods for Machine Learning, PDEs, and Statistics is divided into two parts, theoretical principles and the techniques employed in their applications; contains numerous applications in engineering, finance, and machine learning; and provides a framework for designing numerically efficient, large-scale dataset strategies.

This book will primarily be a resource for researchers and professionals in the fields of scientific computation, statistics, mathematical finance, and engineering sciences. Graduate students in the aforementioned fields may also find this book of use.