{"id":19766,"date":"2002-04-11T00:00:00","date_gmt":"2002-04-11T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/metodos-montecarlo-en-analisis-de-decisiones\/"},"modified":"2002-04-11T00:00:00","modified_gmt":"2002-04-11T00:00:00","slug":"metodos-montecarlo-en-analisis-de-decisiones","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/nacional-de-educacion-a-distancia\/metodos-montecarlo-en-analisis-de-decisiones\/","title":{"rendered":"M\u00e9todos montecarlo en an\u00e1lisis de decisiones"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Miguel Angel Virto Garc\u00eda <\/strong><\/h2>\n<p>En esta memoria se estudia la aplicabilidad de las t\u00e9cnicas de simulaci\u00f3n montecarlo basadas en cadenas de markov (mcmc) para resolver problemas complejos de an\u00e1lisis de decisiones, dise\u00f1ando estrategias que hagan ha dichos m\u00e9todos operativos y eficaces. A partir de un m\u00e9todo de probabilidades ampliadas que requiere muestrear sobre una distribuci\u00f3n artificial conjunta de variables aleatorias y decisiones, los m\u00e9todos mcmc permiten obtener la muestra, de la cual, un posterior an\u00e1lisis sobre la marginal en las decisiones permitir\u00e1 encontrar las mejores alterantivas.  el cap\u00edtulo 2, aborda la gesti\u00f3n de proyectos en entorno aleatorio y con restricciones en los recursos, un problema np-completo que lleva al m\u00e9todo a quedarse atrapado en \u00f3ptimos locales forzando el dise\u00f1o de diversas estrategias modificadas que muestran su eficacia.  los cap\u00edtulos 3 y 4, abordan una serie de problema con decisiones secuenciales. en el cap\u00edtulo 3 se considera el problema m\u00e1s sencillo de la programaci\u00f3n din\u00e1mica estoc\u00e1stica sobre el cual se dise\u00f1a una estrategia original basada en muestrar sobre historias completas e inferir posteriormente las decisiones \u00f3ptimas por un m\u00e9todo de inferencia hac\u00eda atr\u00e1s. Esta misma estrategia, al no requerir ni la propiedad markoviana ni la separabilidad de la utilidad, se emplea en el cap\u00edtulo 4 para resolver problemas m\u00e1s complejos; semi-markovianos y no markovianos. Los algoritmos dise\u00f1ados se muestran eficientes para abordar la complejidad de los modelos desde el punto de vista de la aletaoriedad aunque solo pueden aliviar la complejidad computacional en algunas circunstancias. el trabajo se completa con un anexo sobre duraci\u00f3n de tareas en el pert y otro sobre localizaci\u00f3n de la moda de distribuciones de frecuencia multivariables de distribuciones continuas.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>M\u00e9todos montecarlo en an\u00e1lisis de decisiones<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 M\u00e9todos montecarlo en an\u00e1lisis de decisiones <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Miguel Angel Virto Garc\u00eda <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Nacional de educaci\u00f3n a distancia<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 04\/11\/2002<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>David R\u00edos Insua<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: eduardo Ramos mendez <\/li>\n<li>concha Bielza lozoya (vocal)<\/li>\n<li>sixto R\u00edos insua (vocal)<\/li>\n<li>Antonio Alonso ayuso (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Miguel Angel Virto Garc\u00eda En esta memoria se estudia la aplicabilidad de las t\u00e9cnicas de simulaci\u00f3n montecarlo [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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