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dc.contributor.author
Pulido, Manuel Arturo  
dc.contributor.author
Leeuwen, Peter Jan van  
dc.date.available
2020-05-27T12:46:27Z  
dc.date.issued
2019-05  
dc.identifier.citation
Pulido, Manuel Arturo; Leeuwen, Peter Jan van; Sequential Monte Carlo with kernel embedded mappings: The mapping particle filter; Academic Press Inc Elsevier Science; Journal of Computational Physics; 396; 5-2019; 400-415  
dc.identifier.issn
0021-9991  
dc.identifier.uri
http://hdl.handle.net/11336/105970  
dc.description.abstract
In this work, a novel sequential Monte Carlo filter is introduced which aims at an efficient sampling of the state space. Particles are pushed forward from the prediction to the posterior density using a sequence of mappings that minimizes the Kullback-Leibler divergence between the posterior and the sequence of intermediate densities. The sequence of mappings represents a gradient flow based on the principles of local optimal transport. A key ingredient of the mappings is that they are embedded in a reproducing kernel Hilbert space, which allows for a practical and efficient Monte Carlo algorithm. The kernel embedding provides a direct means to calculate the gradient of the Kullback-Leibler divergence leading to quick convergence using well-known gradient-based stochastic optimization algorithms. Evaluation of the method is conducted in the chaotic Lorenz-63 system, the Lorenz-96 system, which is a coarse prototype of atmospheric dynamics, and an epidemic model that describes cholera dynamics. No resampling is required in the mapping particle filter even for long recursive sequences. The number of effective particles remains close to the total number of particles in all the sequence. Hence, the mapping particle filter does not suffer from sample impoverishment.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Academic Press Inc Elsevier Science  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
STEIN GRADIENT DESCENT  
dc.subject
SEQUENTIAL BAYES  
dc.subject
SWAM OPTIMIZATION  
dc.subject
OPTIMAL TRANSPORT  
dc.subject
KERNEL EMBEDDING  
dc.subject
PARTICLE FLOWS  
dc.subject.classification
Ciencias de la Información y Bioinformática  
dc.subject.classification
Ciencias de la Computación e Información  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Sequential Monte Carlo with kernel embedded mappings: The mapping particle filter  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-05-19T19:02:12Z  
dc.identifier.eissn
1090-2716  
dc.journal.volume
396  
dc.journal.pagination
400-415  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
Cambridge  
dc.description.fil
Fil: Pulido, Manuel Arturo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina. University of Reading; Reino Unido. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas y Naturales y Agrimensura. Departamento de Física; Argentina  
dc.description.fil
Fil: Leeuwen, Peter Jan van. University of Reading; Reino Unido  
dc.journal.title
Journal of Computational Physics  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0021999119304681  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.jcp.2019.06.060