Introduction Data Analysis Uncertainty by Tenorio Luis (18 results)
Language: English
Published by SIAM-Society for Industrial & Applied Mathematics (edition ), 2017
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paperback. Condition: Very Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or limited writing/highlighting. We ship orders daily and Customer Service is our top priority.
Language: English
Published by SIAM-Society for Industrial & Applied Mathematics, 2017
- Softcover
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Condition: good. This is a pre-loved book that shows moderate signs of wear from previous reading. You may notice creases, edge wear, or a cracked spine, but it remains in solid, readable condition. Please note: -May include library or rental stickers, stamps, or markings. -Supplemental materials e.g., CDs, access codes, inserts… are not guaranteed. -Box sets may not come with the original outer box. If it does, the box will not be in perfect condition. Your satisfaction is our top priority! If you have any questions or concerns about your order, please don't hesitate to reach out. Thank you for shopping with us and supporting small businessâ"happy reading.
Language: English
Published by SIAM-Society for Industrial & Applied Mathematics, 2017
- Softcover
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Language: English
Published by SIAM-Society for Industrial & Applied Mathematics, 2017
- Softcover
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Language: English
Published by SIAM-Society for Industrial & Applied Mathematics, 2017
- Softcover
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Language: English
Published by Society for Industrial & Applied Mathematics,U.S., 2017
- Softcover
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Condition: New. 2017. paperback. . . . . .
Language: English
Published by Society for Industrial and Applied Mathematics,U.S., US, 2017
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Paperback. Condition: New. 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 int…roduced 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 ill-posed 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 quantification; Bayesian and non-Bayesian statistical methods and discussions of their complementary roles; and analysis of a real data set to illustrate the methodology covered throughout the book.Audience: This book is intended for senior undergraduates and beginning graduate students in mathematics, engineering and physical sciences. The material spans from undergraduate statistics and probability to data analysis for inverse problems and probability distributions on infinite-dimensional spaces. It is also intended for researchers working on inverse problems and uncertainty quantification in geophysics, astrophysics, physics, and engineering. Because the statistical and probability methods covered have applications beyond inverse problems, the book may also be of interest to those people working in data science or in other applications of uncertainty quantification.
Language: English
Published by MP-SIA SIAM - Society for Industrial and Applied M, 2017
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Language: English
Published by Society For Industrial & Applied Mathematics,U.S., 2017
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Paperback. Condition: Brand New. 269 pages. 10.04x6.93x0.83 inches. In Stock.
Language: English
Published by SIAM-Society for Industrial & Applied Mathematics, 2017
- Softcover
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Language: English
Published by Society for Industrial & Applied Mathematics, 2017
- Softcover
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Condition: New. pp. 279.
Language: English
Published by SIAM-Society for Industrial & Applied Mathematics, 2017
- Softcover
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Language: English
Published by Society for Industrial & Applied Mathematics,U.S., 2017
- Softcover
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Language: English
Published by Society for Industrial & Applied Mathematics,U.S., 2017
- Softcover
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Language: English
Published by SIAM-Society for Industrial & Applied Mathematics, 2017
- Softcover
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Language: English
Published by Society for Industrial and Applied Mathematics,U.S., US, 2017
- Softcover
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Paperback. Condition: New. 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 int…roduced 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 ill-posed 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 quantification; Bayesian and non-Bayesian statistical methods and discussions of their complementary roles; and analysis of a real data set to illustrate the methodology covered throughout the book.Audience: This book is intended for senior undergraduates and beginning graduate students in mathematics, engineering and physical sciences. The material spans from undergraduate statistics and probability to data analysis for inverse problems and probability distributions on infinite-dimensional spaces. It is also intended for researchers working on inverse problems and uncertainty quantification in geophysics, astrophysics, physics, and engineering. Because the statistical and probability methods covered have applications beyond inverse problems, the book may also be of interest to those people working in data science or in other applications of uncertainty quantification.
