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dc.contributor.authorVidal, B.L.
dc.contributor.authorOram, E.
dc.contributor.authorBanos, R.A.
dc.contributor.authorPardo Soto, Luis Carlos
dc.contributor.authorMukhopadhyay, Sajal
dc.contributor.authorFernandez Alonso, Felix
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Física
dc.date.accessioned2018-11-20T13:11:38Z
dc.date.available2018-11-20T13:11:38Z
dc.date.issued2018
dc.identifier.citationVidal, B., Oram, E., Banos, R., Pardo, L., Mukhopadhyay, S., Fernandez-Alonso, F. Bayesian Inference in MANTID - An Update. A: International Collaboration on Advanced Neutron Sources. "Journal of Physics: Conference Series". 2018.
dc.identifier.isbn17426588
dc.identifier.urihttp://hdl.handle.net/2117/124751
dc.description.abstractIn the context of neutron science, Bayesian inference methods have been recently implemented within the MANTID framework [Monserrat D et al. 2015 J. Phys. Conf. Ser. 663 012009 (2015)]. In this contribution, we highlight the advantages of this software package for robust data analysis and subsequent model selection. To this end, we use the celebrated Rosenbrock function to illustrate its merits and strengths relative to classical fitting algorithms. We also introduce the latest additions implemented in MANTID, with a view to increasing its user friendliness as well as stimulating wider use. These include simulated-annealing schemes to reduce the need for initial guesses, as well as new options for multidimensional fitting. © Published under licence by IOP Publishing Ltd.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Física
dc.subject.lcshBayesian statistical decision theory
dc.subject.lcshNeutrons
dc.subject.otherInference engines
dc.subject.otherInternational cooperation
dc.subject.otherNeutron sources
dc.subject.otherNeutrons
dc.subject.otherSimulated annealing
dc.subject.otherBayesian inference
dc.subject.otherFitting algorithms
dc.subject.otherInitial guess
dc.subject.otherModel Selection
dc.subject.otherNeutron science
dc.subject.otherNew options
dc.subject.otherRosenbrock functions
dc.subject.otherUser friendliness
dc.subject.otherBayesian networks
dc.titleBayesian Inference in MANTID - An Update
dc.typeConference report
dc.subject.lemacEstadística bayesiana
dc.subject.lemacNeutrons
dc.contributor.groupUniversitat Politècnica de Catalunya. GCM - Grup de Caracterització de Materials
dc.identifier.doi10.1088/1742-6596/1021/1/012012
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://iopscience.iop.org/article/10.1088/1742-6596/1021/1/012012/meta
dc.rights.accessOpen Access
local.identifier.drac23420736
dc.description.versionPostprint (published version)
local.citation.authorVidal, B.; Oram, E.; Banos, R.; Pardo, L.; Mukhopadhyay, S.; Fernandez-Alonso, F.
local.citation.contributorInternational Collaboration on Advanced Neutron Sources
local.citation.publicationNameJournal of Physics: Conference Series


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