MODELAGEM BAYESIANA DA TEMPERATURA MÁXIMA DO AR EM DIVINÓPOLIS-MG
DOI:
https://doi.org/10.31413/nat.v12i3.17665Keywords:
Distribuição generalizada de valores extremos, Máxima verossimilhança, PrioriAbstract
This research aimed to model the behavior of quarterly maximum temperatures in the city of Divinópolis-MG by fitting the Generalized Extreme Value distribution to the historical series of maximum temperatures using the Maximum Likelihood Estimation and Bayesian Inference methods. Additionally, the study aimed to calculate the return levels of maximum temperature for each return period, evaluating accuracy and mean prediction error (MPE). The return levels were computed using different prior structures, considering informative and non-informative prior distributions, as well as the Maximum Likelihood Estimation method. Analyzing the results of MPE and accuracy, it was observed that Bayesian Inference provided better estimates of maximum temperature compared to the Maximum Likelihood method for all quarters. The informative prior distribution, based on Lavras-MG data, exhibited higher precision in estimating maximum temperatures for the second and third quarters, while the non-informative prior distribution showed higher precision for the first and fourth quarters in the city of Divinópolis-MG.
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