000125093 001__ 125093 000125093 005__ 20230322092645.0 000125093 037__ $$aTAZ-TFG-2022-3434 000125093 041__ $$aspa 000125093 1001_ $$aCamón Fernández, Alejandro 000125093 24200 $$aHamiltonian Monte Carlo methods for estimation of models of climatic series 000125093 24500 $$aMétodos Hamiltonian Monte Carlo para la estimación de modelos de series climáticas 000125093 260__ $$aZaragoza$$bUniversidad de Zaragoza$$c2022 000125093 506__ $$aby-nc-sa$$bCreative Commons$$c3.0$$uhttp://creativecommons.org/licenses/by-nc-sa/3.0/ 000125093 520__ $$aThis work presents a detailed explanation of the HMC algorithm used for bayesian inference and an application to estimate, in the bayesian framework, a new proposed autoregressive model for the maximum daily temperatures.<br />The proposed model is based on the previous work of Castillo-Mateo et al. (2022), where the time structure of mean was modeled. A more flexible estructure is considered modeling also the variance and including interaction terms to reflect a seasonal variability of trend and persistence. The model has easily interpretable terms, it is able to represent the short and long-term dynamics of the temperatures, specially in relation to the effect of a possible climate change. Also a procedure of selection of covariates is designed and all the estimation process is implemented using RStan library, in the R workspace.<br />Models are fitted to series in a database built with data obtained by 18 stations placed in Aragón and its surroundings during a period of over 60 years. The estimation with the HMC-NUTS algorithm is possible but computationally slow. The results show progress towards a more complete model, because both, a non-constant variance and the addition of seasonal-trend and seasonal-persistence interactions, are necessary in Aragón series.<br /><br /> 000125093 521__ $$aGraduado en Matemáticas 000125093 540__ $$aDerechos regulados por licencia Creative Commons 000125093 700__ $$aAsín Lafuente, Jesús$$edir. 000125093 7102_ $$aUniversidad de Zaragoza$$bMétodos Estadísticos$$cEstadística e Investigación Operativa 000125093 8560_ $$f760371@unizar.es 000125093 8564_ $$s5144426$$uhttps://zaguan.unizar.es/record/125093/files/TAZ-TFG-2022-3434.pdf$$yMemoria (spa) 000125093 909CO $$ooai:zaguan.unizar.es:125093$$pdriver$$ptrabajos-fin-grado 000125093 950__ $$a 000125093 951__ $$adeposita:2023-03-21 000125093 980__ $$aTAZ$$bTFG$$cCIEN 000125093 999__ $$a20220912135626.CREATION_DATE