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:
Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Lorenz Biegler, Carnegie Mellon University, USA.
George Biros, Georgia Institute of Technology, USA.
Omar Ghattas, University of Texas at Austin, USA.
Matthias Heinkenschloss, Rice University, USA.
David Keyes, KAUST and Columbia University, USA.
Bani Mallick, Texas A&M University, USA.
Luis Tenorio, Colorado School of Mines, USA.
Bart van Bloemen Waanders, Sandia National Laboratories, USA.
Karen Wilcox, Massachusetts Institute of Technology, USA.
Youssef Marzouk, Massachusetts Institute of Technology, USA.
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:
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. 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:
Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
EUR 59,86 per la spedizione da U.S.A. a Italia
Destinazione, tempi e costiEUR 6,37 per la spedizione da Regno Unito a Italia
Destinazione, tempi e costiDa: Tacoma Book Center, Tacoma, WA, U.S.A.
Hardcover. Condizione: Very Good. Condizione sovraccoperta: 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. Codice articolo 222839
Quantità: 1 disponibili
Da: PBShop.store UK, Fairford, GLOS, Regno Unito
HRD. Condizione: New. New Book. Shipped from UK. Established seller since 2000. Codice articolo FW-9780470697436
Quantità: 15 disponibili
Da: GreatBookPricesUK, Woodford Green, Regno Unito
Condizione: New. Codice articolo 5557181-n
Quantità: Più di 20 disponibili
Da: moluna, Greven, Germania
Gebunden. Condizione: 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. Codice articolo 594698095
Quantità: Più di 20 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 5557181-n
Quantità: Più di 20 disponibili
Da: Books Puddle, New York, NY, U.S.A.
Condizione: New. pp. 388. Codice articolo 26781447
Quantità: 1 disponibili
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
Hardback. Condizione: New. New copy - Usually dispatched within 4 working days. 792. Codice articolo B9780470697436
Quantità: Più di 20 disponibili
Da: Majestic Books, Hounslow, Regno Unito
Condizione: New. pp. 388. Codice articolo 8147800
Quantità: 1 disponibili
Da: Biblios, Frankfurt am main, HESSE, Germania
Condizione: New. pp. 388. Codice articolo 18781453
Quantità: 1 disponibili
Da: CitiRetail, Stevenage, Regno Unito
Hardcover. Condizione: 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. Codice articolo 9780470697436
Quantità: 1 disponibili