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Item Details
| Title:
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LARGE-SCALE INVERSE PROBLEMS AND QUANTIFICATION OF UNCERTAINTY
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| By: |
Lorenz T. Biegler (Editor), George Biros (Editor), Omar Ghattas (Editor) |
| Format: |
Hardback |
| List price:
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£111.95 |
| Our price: |
£100.76 |
| Discount: |
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| You save:
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£11.19 |
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| ISBN 10: |
0470697431 |
| ISBN 13: |
9780470697436 |
| Availability: |
Usually dispatched within 1-3 weeks.
 Delivery
rates
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| Stock: |
Currently 0 available |
| Publisher: |
JOHN WILEY AND SONS LTD |
| Pub. date: |
5 November, 2010 |
| Series: |
Wiley Series in Computational Statistics |
| Pages: |
388 |
| Description: |
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. |
| Synopsis: |
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. |
| Illustrations: |
Illustrations |
| Publication: |
US |
| Imprint: |
Wiley-Blackwell |
| Returns: |
Returnable |
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