dc.creatorPulido, Manuel Arturo
dc.creatorvan Leeuwen, Peter Jan
dc.creatorPosselt, Derek
dc.date.accessioned2021-07-06T12:29:14Z
dc.date.accessioned2022-10-15T07:49:42Z
dc.date.available2021-07-06T12:29:14Z
dc.date.available2022-10-15T07:49:42Z
dc.date.created2021-07-06T12:29:14Z
dc.date.issued2019
dc.identifierKernel Embedded Nonlinear Observational Mappings in the Variational Mapping Particle Filter; 19th International Conference on Computational Science; Faro; Portugal; 2019; 133-133
dc.identifier978-3-030-22747-0
dc.identifierhttp://hdl.handle.net/11336/135543
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4362311
dc.description.abstractRecently, some works have suggested methods to combine variational probabilistic inference with Monte Carlo sampling. One promising approach is via local optimal transport. In this approach, a gradient steepest descent method based on local optimal transport principles is formulated to transform deterministically point samples from an intermediate density to a posterior density. The local mappings that transform the intermediate densities are embedded in a reproducing kernel Hilbert space (RKHS).
dc.languageeng
dc.publisherSpringer
dc.relationinfo:eu-repo/semantics/altIdentifier/url/https://link.springer.com/chapter/10.1007/978-3-030-22747-0_11
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1007/978-3-030-22747-0_11
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.sourceComputational Science: ICCS 2019
dc.subjectSVM
dc.subjectKERNEL EMBDEDING
dc.subjectSEQUENTIAL MONTE CARLO
dc.titleKernel Embedded Nonlinear Observational Mappings in the Variational Mapping Particle Filter
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typeinfo:eu-repo/semantics/conferenceObject
dc.typeinfo:ar-repo/semantics/documento de conferencia


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