Experimental Design for Data Science and Engineering
Theory, experiments, computation, and data are considered as the four pillars of science and engineering. Experimental Design for Data Science and Engineering describes efficient statistical methods for making the experiments cheaper and computations faster for extracting valuable information from d...
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| Formato: | Online |
| Lenguaje: | inglés |
| Publicado: |
Taylor & Francis
2025
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| Materias: | |
| Acceso en línea: | ONIX_20251218T105429_9781040749319_4 |
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| _version_ | 1869514704998629376 |
|---|---|
| author | Joseph, V. Roshan |
| author_browse | Joseph, V. Roshan |
| author_facet | Joseph, V. Roshan |
| author_sort | Joseph, V. Roshan |
| collection | Directory of Open Access Books |
| description | Theory, experiments, computation, and data are considered as the four pillars of science and engineering. Experimental Design for Data Science and Engineering 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. The book also includes recent advances in experimental designs for dealing with large amounts of observational data. Traditionally the design and analysis of physical and computer experiments are treated differently, but this book attempts to create a unified framework using Gaussian process models. Although optimal designs are formulated using Gaussian process models, the focus is on obtaining practical experimental designs that are robust to model assumptions. A wide variety of topics are covered in the book -- from designs for interpolating or integrating simple functions to designs that are useful for optimizing and calibrating complex computer models. It draws techniques that are spread across the fields of statistics, applied mathematics, operations research, uncertainty quantification, and information theory, and build experimental design as a fundamental data analytic tool for engineering and scientific discoveries. Designs for both computer and physical experiments are discussed in a unified framework. Integrates several concepts from numerical analysis, Monte Carlo methods, sensitivity analysis, optimization, and machine learning with experimental design techniques in statistics. Methods are explained using many real experiments from physical sciences and engineering. Experimental design techniques for analysis and compression of big data are discussed. All the numerical illustrations in the book are reproducible using R and Python codes provided in the author’s GitHub site. |
| format | Online |
| id | doab-20.500.12854ir-170340 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Taylor & Francis |
| publisherStr | Taylor & Francis |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1703402025-12-20T05:05:55Z Experimental Design for Data Science and Engineering Joseph, V. Roshan computer experiments Gaussian process modeling Design of Experiments uncertainty quantification Monte Carlo simulation sensitivity analysis fractional factorial methods R and Python examples robust experimental design techniques thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes Theory, experiments, computation, and data are considered as the four pillars of science and engineering. Experimental Design for Data Science and Engineering 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. The book also includes recent advances in experimental designs for dealing with large amounts of observational data. Traditionally the design and analysis of physical and computer experiments are treated differently, but this book attempts to create a unified framework using Gaussian process models. Although optimal designs are formulated using Gaussian process models, the focus is on obtaining practical experimental designs that are robust to model assumptions. A wide variety of topics are covered in the book -- from designs for interpolating or integrating simple functions to designs that are useful for optimizing and calibrating complex computer models. It draws techniques that are spread across the fields of statistics, applied mathematics, operations research, uncertainty quantification, and information theory, and build experimental design as a fundamental data analytic tool for engineering and scientific discoveries. Designs for both computer and physical experiments are discussed in a unified framework. Integrates several concepts from numerical analysis, Monte Carlo methods, sensitivity analysis, optimization, and machine learning with experimental design techniques in statistics. Methods are explained using many real experiments from physical sciences and engineering. Experimental design techniques for analysis and compression of big data are discussed. All the numerical illustrations in the book are reproducible using R and Python codes provided in the author’s GitHub site. 2025-12-20T05:05:54Z 2025-12-20T05:05:54Z 2025-12-18T09:57:12Z 2026 book ONIX_20251218T105429_9781040749319_4 https://library.oapen.org/handle/20.500.12657/109361 9781040749319 9781003661535 9781041117520 9781040749418 https://directory.doabooks.org/handle/20.500.12854/170340 eng Chapman & Hall/CRC Texts in Statistical Science open access image/jpeg Attribution-NonCommercial-NoDerivatives 4.0 International https://library.oapen.org/bitstream/20.500.12657/109361/1/9781040749319.pdf Taylor & Francis Chapman and Hall/CRC 10.1201/9781003661535 10.1201/9781003661535 fa69b019-f4ee-4979-8d42-c6b6c476b5f0 99fd5a6c-bde4-41a2-8c14-9e097061c209 e9f4faa3-9aac-40dd-b63b-aec2d8ab48ad 9781040749319 9781003661535 9781041117520 9781040749418 Chapman and Hall/CRC 246 [...] Georgia Institute of Technology Georgia Tech 10.13039/100006778 open access |
| spellingShingle | computer experiments Gaussian process modeling Design of Experiments uncertainty quantification Monte Carlo simulation sensitivity analysis fractional factorial methods R and Python examples robust experimental design techniques thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes Joseph, V. Roshan Experimental Design for Data Science and Engineering |
| title | Experimental Design for Data Science and Engineering |
| title_full | Experimental Design for Data Science and Engineering |
| title_fullStr | Experimental Design for Data Science and Engineering |
| title_full_unstemmed | Experimental Design for Data Science and Engineering |
| title_short | Experimental Design for Data Science and Engineering |
| title_sort | experimental design for data science and engineering |
| topic | computer experiments Gaussian process modeling Design of Experiments uncertainty quantification Monte Carlo simulation sensitivity analysis fractional factorial methods R and Python examples robust experimental design techniques thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes |
| topic_facet | computer experiments Gaussian process modeling Design of Experiments uncertainty quantification Monte Carlo simulation sensitivity analysis fractional factorial methods R and Python examples robust experimental design techniques thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes |
| url | ONIX_20251218T105429_9781040749319_4 |
| work_keys_str_mv | AT josephvroshan experimentaldesignfordatascienceandengineering |