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Métodos Bayesianos para Modelos Ocultos de Markov en series de tiempo con conteo

dc.contributor.advisorAlonso Malaver, Carlos Eduardospa
dc.contributor.authorDíaz Bonilla, Rafael Eduardospa
dc.contributor.researchgroupProcesos Estocásticosspa
dc.date.accessioned2020-03-05T18:38:25Zspa
dc.date.available2020-03-05T18:38:25Zspa
dc.date.issued2019-12-09spa
dc.description.abstractThis research is dedicated to two special types of Hidden Markov Models (HMM), the first-one dedicated to Poisson Processes (PHMM) and the second-one dedicated to Zero-Inflated Poisson Processes (ZIP-HMM). The two proposed models are Bayesian models for which a package is developed Bayeshmmcts. The estimation process is done using MCMC, Hamiltonian Monte Carlo, NUTS and a new methodology called "the bridge sampler" which is used to solve the unresolved problem of selecting the best model from the Bayesian approach. Finally, we present two applications, the premier we use PHMM for the number of homicides in Colombia-Southamerica and the ZIP-HMM to model the monthly number of Large wildfires (GIF) in Colombia in the period from January 2002 to December 2016.spa
dc.description.abstractEsta investigación se dedica a dos tipos especiales de Modelos Ocultos de Markov (HMM), el primero dedicado a Procesos de Poisson (PHMM) y el segundo dedicado a Procesos de Poisson Cero-Inflados (ZIP-HMM), el enfoque se hace desde la perspectiva Bayesiana, desde la cual se construye un paquete Bayeshmmcts con el fin de ajustar los modelos planteados mediante Métodos de Montecarlo MCMC, Monte Carlo Hamiltoniano y NUTS; unido a lo anterior se utiliza "el muestreador por puente" para resolver el problema no resuelto de la selección del mejor modelo desde el enfoque bayesiano. Finalmente se presentan dos aplicaciones con datos reales de los modelos desarrollados, en los que se sugiere el uso del PHMM para la serie del número de homicidios en Colombia para los años 1960 a 2018, y el ZIP-HMM para modelar la serie mensual número de Grandes Incendios Forestales (GIF) en Colombia en el período enero del 2002 a diciembre del 2016.spa
dc.description.additionalMagister en Estadística. Línea de Investigación: Estadística Bayesiana y Procesos Estocásticosspa
dc.description.degreelevelMaestríaspa
dc.format.extent120spa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationDíaz, R. E. (2019), Métodos bayesianos para modelos ocultos de Markov en series de tiempo con conteo, Tesis maestría, Universidad Nacional de Colombia, sede Bogotáspa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/75888
dc.language.isospaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotáspa
dc.publisher.departmentDepartamento de Estadísticaspa
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dc.rightsDerechos reservados - Universidad Nacional de Colombiaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial 4.0 Internacionalspa
dc.rights.spaAcceso abiertospa
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/spa
dc.subject.ddc510 - Matemáticasspa
dc.subject.proposalModelos ocultos de Markovspa
dc.subject.proposalHidden Markov modelseng
dc.subject.proposalPHMMspa
dc.subject.proposalPHMMeng
dc.subject.proposalZIP HMMeng
dc.subject.proposalZIP HMMspa
dc.subject.proposalBayesian methodseng
dc.subject.proposalmétodos Bayesianosspa
dc.subject.proposalHamiltonian Monte Carloeng
dc.subject.proposalMonte Carlo Hamiltonianospa
dc.subject.proposalMuestreador por Puentespa
dc.subject.proposalBridge Samplingeng
dc.titleMétodos Bayesianos para Modelos Ocultos de Markov en series de tiempo con conteospa
dc.title.alternativeBayesian Methods for Hidden Markov Models in Time Series with Countingspa
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_dc82b40f9837b551spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.versioninfo:eu-repo/semantics/updatedVersionspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

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