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Ajouter au panierGebunden. Etat : 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.
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Ajouter au panierEtat : New. pp. 388.
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Ajouter au panierHardback. Etat : New. New copy - Usually dispatched within 4 working days. 792.
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Edité par John Wiley & Sons Inc, New York, 2010
ISBN 10 : 0470697431 ISBN 13 : 9780470697436
Langue: anglais
Vendeur : CitiRetail, Stevenage, Royaume-Uni
Edition originale
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Ajouter au panierHardcover. Etat : 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.
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierBuch. Etat : 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.
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 166,41
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Ajouter au panierHardcover. Etat : Brand New. 1st edition. 388 pages. 9.25x6.00x1.00 inches. In Stock.
Edité par John Wiley & Sons Inc, New York, 2010
ISBN 10 : 0470697431 ISBN 13 : 9780470697436
Langue: anglais
Vendeur : Grand Eagle Retail, Mason, OH, Etats-Unis
Edition originale
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Ajouter au panierHardcover. Etat : 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 multiple locations in the US or from the UK, depending on stock availability.
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Edité par John Wiley & Sons Inc, New York, 2010
ISBN 10 : 0470697431 ISBN 13 : 9780470697436
Langue: anglais
Vendeur : AussieBookSeller, Truganina, VIC, Australie
Edition originale
EUR 209,10
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Ajouter au panierHardcover. Etat : 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.
Edité par John Wiley and Sons Ltd, 2010
ISBN 10 : 0470697431 ISBN 13 : 9780470697436
Langue: anglais
Vendeur : OM Books, Sevilla, SE, Espagne
EUR 360
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Edité par Wiley; 1st edition (June 24, 2011), 2011
ISBN 10 : 0470697431 ISBN 13 : 9780470697436
Vendeur : Tacoma Book Center, Tacoma, WA, Etats-Unis
Edition originale
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Ajouter au panierHardcover. Etat : Very Good. Etat de la jaquette : No Dustjacket. First Edition. ISBN 9780470697436. Hardback. No dustjacket. Bound in Pictorial Boards. Near Fine condition. Tight bright attractive copy with no markings to the book. As new condition. This book is Large-Scale Inverse Problems and Quantification of Uncertainty (Wiley Series in Computational Statistics Book 713). No Signature.