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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Bibliographic Details

Main Author:	Joseph, V. Roshan
Format:	Online
Language:	English
Published:	Taylor & Francis 2025
Subjects:	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
Online Access:	ONIX_20251218T105429_9781040749319_4
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