@prefix dcat: . @prefix dct: . @prefix foaf: . @prefix vcard: . @prefix xsd: . a dcat:Dataset ; dct:description """ This thesis deals with the problem of global optimization of expensive-to-evaluate functions in a Bayesian framework. We say that a function is expensive-to-evaluate when its evaluation requires a significant amount of resources (e.g., very long numerical simulations).In this context, it is important to use optimization algorithms that can deal with a limited number of function evaluations. We consider here a Bayesian approach which consists in assigning a prior to the function, under the form of a Gaussian random process. The idea is then to choose the next evaluation points using a probabilistic criterion that indicates, conditional on the previous evaluations, the most interesting regions of the research domain for the optimizer. Two difficulties in this approach can be identified: the choice of the Gaussian process prior and the maximization of the criterion. The first problem is usually solved by using a maximum likelihood approach, which turns out to be a poorly robust method, and to which we prefer a fully Bayesian approach. The contribution of this work is the introduction of a new Bayesian optimization algorithm, which maximizes the Expected Improvement (EI) criterion, and provides an answer to both problems thanks to a Sequential Monte Carlo approach. Numerical results on benchmark tests show good performances of our algorithm compared to those of several other methods of the literature. """ ; dct:identifier "NNT: 2013SUPL0011" ; dct:issued "2026-05-09T15:54:25.446472"^^xsd:dateTime ; dct:language "fr" ; dct:modified "2026-05-09T15:54:25.446477"^^xsd:dateTime ; dct:publisher ; dct:title "New Bayesian optimization algorithm using a sequential Monte-Carlo approach" ; dcat:contactPoint [ a vcard:Organization ; vcard:fn "CCSD" ] ; dcat:distribution ; dcat:keyword "critere-ei", "expected-improvement-criterion", "gaussian-process", "infoeu-reposemanticsdoctoralthesis", "krigeage", "kriging", "methodes-smc", "optimisation", "optimization", "processus-gaussien", "smc-methods", "spiotherengineering-sciences-physicsother", "theses" ; dcat:landingPage . a dcat:Distribution ; dct:format "HTML" ; dct:issued "2026-05-09T15:54:25.454275"^^xsd:dateTime ; dct:modified "2026-05-09T15:54:25.434708"^^xsd:dateTime ; dct:title "New Bayesian optimization algorithm using a sequential Monte-Carlo approach" ; dcat:accessURL . a foaf:Agent ; foaf:name "test_moissonnage_selune" . a foaf:Document .