Cantitate/Preț
Produs

White Noise Distribution Theory: Probability and Stochastics Series

Autor Hui-Hsiung Kuo
en Limba Engleză Paperback – 30 iun 2020
Learn the basics of white noise theory with White Noise Distribution Theory. This book covers the mathematical foundation and key applications of white noise theory without requiring advanced knowledge in this area. This instructive text specifically focuses on relevant application topics such as integral kernel operators, Fourier transforms, Laplacian operators, white noise integration, Feynman integrals, and positive generalized functions. Extremely well-written by one of the field's leading researchers, White Noise Distribution Theory is destined to become the definitive introductory resource on this challenging topic.
Citește tot Restrânge

Din seria Probability and Stochastics Series

Preț: 31272 lei

Preț vechi: 35769 lei
-13% Nou

Puncte Express: 469

Preț estimativ în valută:
5985 6314$ 4988£

Carte tipărită la comandă

Livrare economică 02-16 ianuarie 25

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9780367579616
ISBN-10: 0367579618
Pagini: 400
Dimensiuni: 156 x 234 mm
Greutate: 0.55 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press
Seria Probability and Stochastics Series


Public țintă

Professional

Cuprins

Introduction to White Noise. Background. White Noise as an Infinite Dimensional Calculus. Constructions of Test and Generalized Functions. The S-Transform. Continuous Versions and Analytic Extensions. Delta Functions. Characterization Theorems. Differential Operators. Integral Kernel Operators. Fourier Transforms. Laplacian Operators. White Noise Integration. Feynman Integrals. Positive Generalized Functions. Appendix A: Notes and Comments. Appendix B: Miscellaneous Formulas.

NTI/Sales Copy

Recenzii

"It is precisely written, up to date, and makes frequent appeal to the research literature."
-The Mathematical Gazette

Descriere

Learn the basics of white noise theory with White Noise Distribution Theory. This book covers the mathematical foundation and key applications of white noise theory without requiring advanced knowledge in this area.