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dc.contributor.authorCandia García, Cristian
dc.contributor.authorForero, Manuel G.
dc.contributor.authorHerrera Rivera, Sergio
dc.date.accessioned2021-06-25T20:50:44Z
dc.date.accessioned2021-10-01T17:37:41Z
dc.date.available2021-06-25T20:50:44Z
dc.date.available2021-10-01T17:37:41Z
dc.date.issued2019
dc.identifier.isbn978-3-030-13468-6
dc.identifier.isbn978-3-030-13469-3
dc.identifier.urihttps://repositorio.escuelaing.edu.co/handle/001/1605
dc.description.abstractWhen modeling phenomena that cannot be studied by deterministic analytical approaches, one of the main tasks is to generate random variates. The widely-used techniques, such as the inverse transformation, convolution, and rejection-acceptance methods, involve a significant amount of statistical work and do not provide satisfactory results when the data do not conform to the known probability density functions. This study aims to propose an alternative nonparametric method for generating random variables that combines kernel density estimation (KDE), and radial basis function based neural networks (RBFBNNs). We evaluate the method’s performance using Poisson, triangular, and exponential probability density distributions and assessed its utility for unknown distributions. The results show that the model’s effectiveness depends substantially on selecting an appropriate bandwidth value for KDE and a certain minimum number of data points to train the algorithm. the proposed method enabled us to achieve an R2 value between 0.91 and 0.99 for analyzed distributions.eng
dc.description.abstractCuando se modelan fenómenos que no se pueden estudiar mediante enfoques analíticos deterministas, una de las principales tareas es generar variables aleatorias. Las técnicas ampliamente utilizadas, como los métodos de transformación inversa, convolución y rechazo-aceptación, involucran una cantidad significativa de trabajo estadístico y no brindan resultados satisfactorios cuando los datos no se ajustan a las funciones de densidad de probabilidad conocidas. Este estudio tiene como objetivo proponer un método no paramétrico alternativo para generar variables aleatorias que combine la estimación de la densidad del kernel (KDE) y las redes neuronales basadas en funciones de base radial (RBFBNN). Evaluamos el rendimiento del método usando distribuciones de densidad de probabilidad Poisson, triangular y exponencial y evaluamos su utilidad para distribuciones desconocidas. Los resultados muestran que la efectividad del modelo depende sustancialmente de seleccionar un valor de ancho de banda apropiado para KDE y un cierto número mínimo de puntos de datos para entrenar el algoritmo. el método propuesto nos permitió alcanzar un valor de R2 entre 0,91 y 0,99 para las distribuciones analizadas.spa
dc.format.extent8 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherSpringer Naturespa
dc.relation.ispartofseriesLNCS;11401
dc.sourcehttps://link.springer.com/chapter/10.1007%2F978-3-030-13469-3_29spa
dc.titleGenerating Random Variates via Kernel Density Estimation and Radial Basis Function Based Neural Networkseng
dc.typeCapítulo - Parte de Librospa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_16ecspa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.identifier.doi10.1007/978-3-030-13469-3_29
dc.identifier.urlhttps://doi.org/10.1007/978-3-030-13469-3_29
dc.publisher.placeSuizaspa
dc.relation.citationeditionIberoamerican Congress on Pattern Recognition CIARP 2018: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications pp 245-252spa
dc.relation.citationendpage252spa
dc.relation.citationstartpage245spa
dc.relation.indexedN/Aspa
dc.relation.ispartofbookProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applicationsspa
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dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.subject.armarcVariable aleatoriaspa
dc.subject.armarcDistribución de probabilidadspa
dc.subject.armarcTeoría de las distribuciones (Análisis funcional)spa
dc.subject.armarcTheory of distributions (Functional analysis)eng
dc.subject.armarcFunciones Kernelspa
dc.subject.armarcKernel functionseng
dc.subject.proposalGeneral regression neural networkeng
dc.subject.proposalProbabilistic neural networkeng
dc.subject.proposalKernel density estimationeng
dc.subject.proposalRandom variableeng
dc.subject.proposalProbability distributioneng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/bookPartspa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa


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