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Inverse Problem Theory and Methods for Model Parameter Estimation
Albert Tarantola
€ 153.30
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Description for Inverse Problem Theory and Methods for Model Parameter Estimation
Paperback.
The use of actual observations to infer the properties of a model is an inverse problem, which are often difficult as they may not have a unique solution. This book proposes a general approach that is valid for linear as well as for nonlinear problems. The philosophy is essentially probabilistic and allows the reader to understand the basic difficulties appearing in the resolution of inverse problems. The book attempts to explain how a method of acquisition of information can be applied to actual real-world problems, including many heuristic arguments. Prompted by recent developments in inverse theory, this text is a ... Read more
The use of actual observations to infer the properties of a model is an inverse problem, which are often difficult as they may not have a unique solution. This book proposes a general approach that is valid for linear as well as for nonlinear problems. The philosophy is essentially probabilistic and allows the reader to understand the basic difficulties appearing in the resolution of inverse problems. The book attempts to explain how a method of acquisition of information can be applied to actual real-world problems, including many heuristic arguments. Prompted by recent developments in inverse theory, this text is a ... Read more
Product Details
Format
Paperback
Publication date
2004
Publisher
SIAM: Society for Industrial and Applied Mathematics
Number of pages
352
Condition
New
Number of Pages
352
Place of Publication
New York, United States
ISBN
9780898715729
SKU
V9780898715729
Shipping Time
Usually ships in 7 to 11 working days
Ref
99-1
Reviews for Inverse Problem Theory and Methods for Model Parameter Estimation
'Tarantola's book is undoubtedly the most important work on probabilistic inverse theory. It presents a complete theory where a physical parameter is represented, not by a number, but by a probability distribution.' Klaus Mosegaard, Niels Bohr Institute