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001440219 001__ 1440219
001440219 003__ OCoLC
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001440219 020__ $$a9783030824587$$q(electronic bk.)
001440219 020__ $$a3030824586$$q(electronic bk.)
001440219 020__ $$z9783030824570
001440219 020__ $$z3030824578
001440219 0247_ $$a10.1007/978-3-030-82458-7$$2doi
001440219 035__ $$aSP(OCoLC)1273967004
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001440219 049__ $$aISEA
001440219 050_4 $$aQA279$$b.P68 2021
001440219 08204 $$a519.5$$223
001440219 1001_ $$aPourmohamad, Tony,$$eauthor.
001440219 24510 $$aBayesian optimization with application to computer experiments /$$cTony Pourmohamad, Herbert K.H. Lee.
001440219 264_1 $$aCham :$$bSpringer,$$c[2021]
001440219 264_4 $$c©2021
001440219 300__ $$a1 online resource :$$billustrations (chiefly color)
001440219 336__ $$atext$$btxt$$2rdacontent
001440219 337__ $$acomputer$$bc$$2rdamedia
001440219 338__ $$aonline resource$$bcr$$2rdacarrier
001440219 347__ $$atext file
001440219 347__ $$bPDF
001440219 4901_ $$aSpringerBriefs in statistics
001440219 504__ $$aIncludes bibliographical references.
001440219 5050_ $$a1. Computer experiments -- 2. Surrogate models -- 3. Unconstrained optimization -- 4. Constrained optimization.
001440219 506__ $$aAccess limited to authorized users.
001440219 520__ $$aThis book introduces readers to Bayesian optimization, highlighting advances in the field and showcasing its successful applications to computer experiments. R code is available as online supplementary material for most included examples, so that readers can better comprehend and reproduce methods. Compact and accessible, the volume is broken down into four chapters. Chapter 1 introduces the reader to the topic of computer experiments; it includes a variety of examples across many industries. Chapter 2 focuses on the task of surrogate model building and contains a mix of several different surrogate models that are used in the computer modeling and machine learning communities. Chapter 3 introduces the core concepts of Bayesian optimization and discusses unconstrained optimization. Chapter 4 moves on to constrained optimization, and showcases some of the most novel methods found in the field. This will be a useful companion to researchers and practitioners working with computer experiments and computer modeling. Additionally, readers with a background in machine learning but minimal background in computer experiments will find this book an interesting case study of the applicability of Bayesian optimization outside the realm of machine learning.
001440219 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 13, 2021).
001440219 650_0 $$aExperimental design.
001440219 650_0 $$aComputer science$$xExperiments.
001440219 650_0 $$aBayesian statistical decision theory.
001440219 650_6 $$aPlan d'expérience.
001440219 650_6 $$aInformatique$$xExpériences.
001440219 650_6 $$aThéorie de la décision bayésienne.
001440219 655_0 $$aElectronic books.
001440219 7001_ $$aLee, Herbert K. H.,$$eauthor.
001440219 77608 $$iPrint version:$$aPourmohamad, Tony.$$tBayesian optimization with application to computer experiments.$$dCham : Springer, [2021]$$z3030824578$$z9783030824570$$w(OCoLC)1258674093
001440219 830_0 $$aSpringerBriefs in statistics.
001440219 852__ $$bebk
001440219 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-82458-7$$zOnline Access$$91397441.1
001440219 909CO $$ooai:library.usi.edu:1440219$$pGLOBAL_SET
001440219 980__ $$aBIB
001440219 980__ $$aEBOOK
001440219 982__ $$aEbook
001440219 983__ $$aOnline
001440219 994__ $$a92$$bISE