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Surrogate: Gaußsche Prozessmodellierung, Design und Optimierung für die angewandten -
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eBay-Artikelnr.:365722650740
Artikelmerkmale
- Artikelzustand
- ISBN-13
- 9780367415426
- Book Title
- Surrogates
- ISBN
- 9780367415426
Über dieses Produkt
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
0367415429
ISBN-13
9780367415426
eBay Product ID (ePID)
15038309108
Product Key Features
Number of Pages
560 Pages
Language
English
Publication Name
Surrogates : Gaussian Process Modeling, Design, and Optimization for the Applied Sciences
Publication Year
2020
Subject
Probability & Statistics / General, Probability & Statistics / Regression Analysis, Probability & Statistics / Multivariate Analysis, General
Type
Textbook
Subject Area
Mathematics
Series
Chapman and Hall/Crc Texts in Statistical Science Ser.
Format
Hardcover
Dimensions
Item Height
1.4 in
Item Weight
43.2 Oz
Item Length
10 in
Item Width
7.3 in
Additional Product Features
Intended Audience
College Audience
LCCN
2019-042570
Reviews
"The coverage of this book is unique and important. It focuses on a current area at the edge of applied mathematics and statistics, a domain that really should be substantially better-developed. For researchers and students who already have a solid foundation in statistics and familiarity with R, and want to know more about how statistics can be used in the approximation of complex functions and numerical optimization (i.e. computer experiments), this should be a welcome resource." -Max Morris, Iowa State University, USA "This book is a fantastic exploration of Gaussian process surrogates and a variety of applications to which they have been utilized. This approach is rapidly expanding in both the statistical and machine learning communities. I particularly enjoyed the applied focus of this book and the ease with which the author enables the reader to "follow along", by providing code for each example discussed. In my view, the technical content of the book is well-chosen, and the flow of material should be very well-received by the readership." -Brian J. Williams, Scientist, Los Alamos National Laboratory
Illustrated
Yes
Table Of Content
1 Historical Perspective 2 Four Motivating Datasets 3 Steepest Ascent and Ridge Analysis 4 Space-filling Design 5 Gaussian process regression 6 Model-Based Design for GPs 7 Optimization 8 Calibration and Sensitivity 9 GP Fidelity and Scale 10 Heteroskedasticity Appendix A Numerical Linear Algebra for Fast GPs Appendix B An Experiment Game
Synopsis
Surrogates: a graduate textbook, or professional handbook, on topics at the interface between machine learning, spatial statistics, computer simulation, meta-modeling (i.e., emulation), design of experiments, and optimization. Experimentation through simulation, "human out-of-the-loop" statistical support (focusing on the science), management of dynamic processes, online and real-time analysis, automation, and practical application are at the forefront. Topics include: Gaussian process (GP) regression for flexible nonparametric and nonlinear modeling. Applications to uncertainty quantification, sensitivity analysis, calibration of computer models to field data, sequential design/active learning and (blackbox/Bayesian) optimization under uncertainty. Advanced topics include treed partitioning, local GP approximation, modeling of simulation experiments (e.g., agent-based models) with coupled nonlinear mean and variance (heteroskedastic) models. Treatment appreciates historical response surface methodology (RSM) and canonical examples, but emphasizes contemporary methods and implementation in R at modern scale. Rmarkdown facilitates a fully reproducible tour, complete with motivation from, application to, and illustration with, compelling real-data examples. Presentation targets numerically competent practitioners in engineering, physical, and biological sciences. Writing is statistical in form, but the subjects are not about statistics. Rather, they're about prediction and synthesis under uncertainty; about visualization and information, design and decision making, computing and clean code., Surrogates is a graduate textbook, on topics at the interface between machine learning,spatial statistics,computer simulation,meta-modeling,design of experiments,and optimization. Experimentation through simulation,management of dynamic processes,online and real-time analysis,automation and practical application are at the forefront.
LC Classification Number
QA274.4.G73 2020
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