ISBN: 9780470697436 - large-scale inverse problems and quantification of uncertainty (wiley series in computational statistics) (7 Ergebnisse)

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. pp. 388.

Zustand: New. In English.

Zustand: New. This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman f.

Hardcover. Zustand: Brand New. 1st edition. 388 pages. 9.25x6.00x1.00 inches. In Stock.

Zustand: New. This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. Editor(s): Biegler, Lorenz T.; Biros, George; Ghattas, Omar; Heinkenschloss, Matthias; Keyes, David; Mallick, Bani K.; Tenorio, Luis; Van Bloemen Waanders, Bart; Wilcox, Karen; Marzouk, Youssef. Series: Wiley Series in Computational Statistics. Num Pages: 388 pages, Illustrations. BIC Classification: PBKJ. Category: (P) Professional & Vocational. Dimension: 238 x 159 x 23. Weight in Grams: 760. . 2010. . . . . Books ship from the US and Ireland.…

Buch. Zustand: Neu. Neuware - This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: - Brings together the perspectives of researchers in areas of inverse problems and data assimilation. - Assesses the current state-of-the-art and identify needs and opportunities for future research. - Focuses on the computational methods used to analyze and simulate inverse problems. - Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.…