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

dc.contributorAlonso Malaver, Carlos Eduardo
dc.contributorProcesos Estocásticos
dc.creatorDíaz Bonilla, Rafael Eduardo
dc.date.accessioned2020-03-05T18:38:25Z
dc.date.available2020-03-05T18:38:25Z
dc.date.created2020-03-05T18:38:25Z
dc.date.issued2019-12-09
dc.identifierDíaz, R. E. (2019), Métodos bayesianos para modelos ocultos de Markov en series de tiempo con conteo, Tesis maestría, Universidad Nacional de Colombia, sede Bogotá
dc.identifierhttps://repositorio.unal.edu.co/handle/unal/75888
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.
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.
dc.languagespa
dc.publisherDepartamento de Estadística
dc.publisherUniversidad Nacional de Colombia - Sede Bogotá
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dc.rightsAtribución-NoComercial 4.0 Internacional
dc.rightsAcceso abierto
dc.rightshttp://creativecommons.org/licenses/by-nc/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightsDerechos reservados - Universidad Nacional de Colombia
dc.titleMétodos Bayesianos para Modelos Ocultos de Markov en series de tiempo con conteo
dc.typeOtro


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