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Experimental design for data science and engineering /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Experimental design for data science and engineering /V. Roshan Joseph.
作者:	Joseph, V. Roshan,
出版者:	Boca Raton :CRC Press,2026.
面頁冊數:	xii, 233 p. :col. bill., tables, charts ;27 cm.
標題:	EngineeringStatistical methods.
ISBN:	9781041117520 :

Experimental design for data science and engineering /
Joseph, V. Roshan,

Experimental design for data science and engineering /V. Roshan Joseph. - Boca Raton :CRC Press,2026. - xii, 233 p. :col. bill., tables, charts ;27 cm. - Chapman & Hall/CRC texts in statistical science. - Chapman & Hall/CRC texts in statistical science..

Includes bibliographical references and index.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- I. Introduction -- 1. Experiments -- 1.1. Overview -- 1.2. Applications -- 1.3. Role of Uncertainty -- 1.4. Outline of the Book -- 2. Modeling Techniques -- 2.1. Interpolation -- 2.1.1. Polynomial Interpolation -- 2.1.2. Radial Basis Functions -- 2.1.3. Kriging -- 2.1.4. Gaussian Process Modeling -- 2.2. Regression -- 2.2.1. Linear Regression -- 2.2.2. Kernel Ridge Regression -- 2.2.3. Gaussian Process Regression -- II. Computer Experiments -- 3. Model-based Designs -- 3.1. Prediction-based Designs -- 3.2. Maximum Entropy Designs -- 4. Space-filling Designs -- 4.1. Clustering-based Designs -- 4.2. Minimax Designs -- 4.3. Maximin Designs -- 4.4. Latin Hypercube Designs -- 4.5. MaxPro Designs -- 4.5.1. Constrained Regions -- 4.5.2. Qualitative Factors -- 4.5.3. Branching Designs -- 4.5.4. Nested Designs -- 4.6. Minimum Energy Designs -- 4.6.1. Bayesian Computation -- 4.6.2. Candidate Set Generation -- 4.6.3. Continuous Fidelity Parameter -- 5. Representative Points -- 5.1. Uniform Design -- 5.1.1. Uniformity -- 5.1.2. Integration -- 5.2. Non-uniform Distributions -- 5.2.1. Transformation Methods -- 5.2.2. Support Points -- 5.2.3. Uncertainty Propagation -- 5.2.4. Population Quasi-Monte Carlo -- 6. Screening Designs -- 6.1. Sensitivity Analysis -- 6.1.1. Variance-based Sensitivity Measures -- 6.1.2. Derivative-based Sensitivity Measures -- 6.2. Morris Screening Design -- 6.3. MOFAT Designs -- 7. Sequential Designs -- 7.1. Emulation -- 7.1.1. Prediction-based Sequential Design -- 7.1.2. Entropy-based Sequential Design -- 7.2. Optimization -- 7.3. Inverse Designs -- III. Physical Experiments -- 8. Fractional Factorial Designs -- 8.1. Two-level Designs -- 8.2. Bayesian-inspired Designs -- 8.3. Designs with More than Two Levels.

This book describes efficient statistical methods for making the experiments cheaper and computations faster for extracting valuable information from data and help identify discrepancies in the theory. It also includes recent advances in experimental designs for dealing with large amounts of observational data.

ISBN: 9781041117520 :NT$3514Subjects--Topical Terms:

181883
Engineering
--Statistical methods.

LC Class. No.: TA340 / .J67 2026

Experimental design for data science and engineering /
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245 1 0 $a Experimental design for data science and engineering / $c V. Roshan Joseph.
260 $a Boca Raton : $b CRC Press, $c 2026.
300 $a xii, 233 p. : $b col. bill., tables, charts ; $c 27 cm.
490 1 $a Chapman & Hall/CRC texts in statistical science
504 $a Includes bibliographical references and index.
505 0 $a Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- I. Introduction -- 1. Experiments -- 1.1. Overview -- 1.2. Applications -- 1.3. Role of Uncertainty -- 1.4. Outline of the Book -- 2. Modeling Techniques -- 2.1. Interpolation -- 2.1.1. Polynomial Interpolation -- 2.1.2. Radial Basis Functions -- 2.1.3. Kriging -- 2.1.4. Gaussian Process Modeling -- 2.2. Regression -- 2.2.1. Linear Regression -- 2.2.2. Kernel Ridge Regression -- 2.2.3. Gaussian Process Regression -- II. Computer Experiments -- 3. Model-based Designs -- 3.1. Prediction-based Designs -- 3.2. Maximum Entropy Designs -- 4. Space-filling Designs -- 4.1. Clustering-based Designs -- 4.2. Minimax Designs -- 4.3. Maximin Designs -- 4.4. Latin Hypercube Designs -- 4.5. MaxPro Designs -- 4.5.1. Constrained Regions -- 4.5.2. Qualitative Factors -- 4.5.3. Branching Designs -- 4.5.4. Nested Designs -- 4.6. Minimum Energy Designs -- 4.6.1. Bayesian Computation -- 4.6.2. Candidate Set Generation -- 4.6.3. Continuous Fidelity Parameter -- 5. Representative Points -- 5.1. Uniform Design -- 5.1.1. Uniformity -- 5.1.2. Integration -- 5.2. Non-uniform Distributions -- 5.2.1. Transformation Methods -- 5.2.2. Support Points -- 5.2.3. Uncertainty Propagation -- 5.2.4. Population Quasi-Monte Carlo -- 6. Screening Designs -- 6.1. Sensitivity Analysis -- 6.1.1. Variance-based Sensitivity Measures -- 6.1.2. Derivative-based Sensitivity Measures -- 6.2. Morris Screening Design -- 6.3. MOFAT Designs -- 7. Sequential Designs -- 7.1. Emulation -- 7.1.1. Prediction-based Sequential Design -- 7.1.2. Entropy-based Sequential Design -- 7.2. Optimization -- 7.3. Inverse Designs -- III. Physical Experiments -- 8. Fractional Factorial Designs -- 8.1. Two-level Designs -- 8.2. Bayesian-inspired Designs -- 8.3. Designs with More than Two Levels.
505 0 $a 8.4. Mixture Designs -- 9. Model Calibration -- 9.1. Nonlinear Optimal Designs -- 9.1.1. Analytical Models -- 9.1.2. Computationally Expensive Models -- 9.2. Robust Designs -- 9.2.1. Parameter Uncertainties -- 9.2.2. Model Uncertainties -- IV. Data Science -- 10. Data Subsampling -- 10.1. Support Points-based Subsampling -- 10.2. Data Splitting -- 10.3. Data Twinning -- 10.4. Supervised Compression -- 11. Data Analysis -- 11.1. Factor Selection and Ranking -- 11.2. Twin-Gaussian Process -- Bibliography -- Index.
520 $a This book describes efficient statistical methods for making the experiments cheaper and computations faster for extracting valuable information from data and help identify discrepancies in the theory. It also includes recent advances in experimental designs for dealing with large amounts of observational data.
650 0 $a Engineering $x Statistical methods. $3 181883
650 0 $a Experimental design. $3 181887
830 0 $a Chapman & Hall/CRC texts in statistical science. $3 1011698