An introduction to data analysis and uncertainty quantification for inverse problems
 Responsibility
 Luis Tenorio, Colorado School of Mines, Golden, Colorado.
 Publication
 Philadelphia : Society for Industrial and Applied Mathematics, [2017]
 Physical description
 ix, 269 pages ; 26 cm.
 Series
 Mathematics in industry ; 3.
At the library
Science Library (Li and Ma)
Stacks
Call number  Status 

QA378.5 .T46 2017  Unknown 
More options
Description
Creators/Contributors
 Author/Creator
 Tenorio, Luis, author.
Contents/Summary
 Bibliography
 Includes bibliographical references and index.
 Contents

 Preface
 Chapter 1: An introduction to inverse problems
 Chapter 2: A primer on statistical methods
 Chapter 3: Applications to inverse problems I
 Chapter 4: Applications to inverse problems II
 Chapter 5: A nonlinear parameter estimation problemAppendix A: Some results from analysisAppendix B: Conditional probability and expectationBibliographyAuthor IndexGeneral Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 ["Inverse problems are found in many applications, such as medical imaging, engineering, astronomy, and geophysics, among others. To solve an inverse problem is to recover an object from noisy, usually indirect observations. Solutions to inverse problems are subject to many potential sources of error introduced by approximate mathematical models, regularization methods, numerical approximations for efficient computations, noisy data, and limitations in the number of observations; thus it is important to include an assessment of the uncertainties as part of the solution. Such assessment is interdisciplinary by nature, as it requires, in addition to knowledge of the particular application, methods from applied mathematics, probability, and statistics.This book bridges applied mathematics and statistics by providing a basic introduction to probability and statistics for uncertainty quantification in the context of inverse problems, as well as an introduction to statistical regularization of inverse problems. The author covers basic statistical inference, introduces the framework of illposed inverse problems, and explains statistical questions that arise in their applications.An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems includes:many examples that explain techniques which are useful to address general problems arising in uncertainty quantificationBayesian and nonBayesian statistical methods and discussions of their complementary roles, andanalysis of a real data set to illustrate the methodology covered throughout the book.", {"source"=>"(source: Nielsen Book Data)"}, "9781611974911", "20170907"]
Subjects
Bibliographic information
 Publication date
 2017
 Series
 Mathematics in industry ; MN03
 Note
 Numbering from 490, taken from spine of book.
 ISBN
 9781611974911 paperback
 1611974917 paperback