greenhill-bayesianoptimization-2020.pdf (7.24 MB)
Bayesian optimization for adaptive experimental design: a review
journal contribution
posted on 2020-01-01, 00:00 authored by Stewart GreenhillStewart Greenhill, Santu RanaSantu Rana, Sunil GuptaSunil Gupta, Pratibha Vellanki, Svetha VenkateshSvetha VenkateshBayesian optimisation is a statistical method that efficiently models and optimises expensive 'black-box' functions. This review considers the application of Bayesian optimisation to experimental design, in comparison to existing Design of Experiments (DOE) methods. Solutions are surveyed for a range of core issues in experimental design including: the incorporation of prior knowledge, high dimensional optimisation, constraints, batch evaluation, multiple objectives, multi-fidelity data, and mixed variable types.
History
Journal
IEEE accessVolume
8Pagination
13937 - 13948Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
Link to full text
ISSN
2169-3536eISSN
2169-3536Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
Categories
No categories selectedKeywords
Science & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringBayesian methodsdesign for experimentsdesign optimizationmachine learning algorithmsPREDICTIVE ENTROPY SEARCHMULTIOBJECTIVE OPTIMIZATIONEFFICIENTSUPPORTLOOP
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC