Spectral Methods For Data Science
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Spectral Methods for Data Science
This monograph presents a systematic, yet accessible introduction to spectral methods from a modern statistical perspective. It is essential reading for all students, researchers and practitioners working in Data Science.
Spectral Methods
Author: Jie Shen
language: en
Publisher: Springer Science & Business Media
Release Date: 2011-08-25
Along with finite differences and finite elements, spectral methods are one of the three main methodologies for solving partial differential equations on computers. This book provides a detailed presentation of basic spectral algorithms, as well as a systematical presentation of basic convergence theory and error analysis for spectral methods. Readers of this book will be exposed to a unified framework for designing and analyzing spectral algorithms for a variety of problems, including in particular high-order differential equations and problems in unbounded domains. The book contains a large number of figures which are designed to illustrate various concepts stressed in the book. A set of basic matlab codes has been made available online to help the readers to develop their own spectral codes for their specific applications.
Spectral Methods for Data Science
This monograph presents a systematic, yet accessible introduction to spectral methods from a modern statistical perspective. It is essential reading for all students, researchers and practitioners working in Data Science.