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Large-Scale Inverse Problems and Quantification of Uncertainty
. Ed(S): Biegler, Lorenz T.; Biros, George; Ghattas, Omar; Heinkenschloss, Matthias; Keyes, David; Mallick, Bani K.; Tenorio, Luis; Van Bloemen Waand
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Description for Large-Scale Inverse Problems and Quantification of Uncertainty
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.
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.
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 ... Read more
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.
Show LessProduct Details
Publication date
2010
Publisher
John Wiley and Sons Ltd United States
Number of pages
388
Condition
New
Series
Wiley Series in Computational Statistics
Number of Pages
400
Format
Hardback
Place of Publication
New York, United States
ISBN
9780470697436
SKU
V9780470697436
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-50
About . Ed(S): Biegler, Lorenz T.; Biros, George; Ghattas, Omar; Heinkenschloss, Matthias; Keyes, David; Mallick, Bani K.; Tenorio, Luis; Van Bloemen Waand
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. ... Read more
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