biegler lorenz biros george ghattas (17 Ergebnisse)

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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. . . . .

Hardcover. Zustand: new. Hardcover. 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. 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. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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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.

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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.

Hardcover. Zustand: new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.