Bayesian Fitting of Univariate Skew Normal Distribution on Padua Temperature Dataset
DOI:
https://doi.org/10.54065/likelihood.1054Keywords:
Bayesian, Skew Normal, Temperature Data ModelingAbstract
Data-driven distributional exploration is an important issue in selecting appropriate statistical methods. Due to the presence of skewness that deviates from the normal distribution, the annual average temperature data of Padua cannot be adequately fitted using a symmetric distribution such as the normal distribution. To address this issue, this study aims to fit a skew normal distribution on Padua temperature dataset. Under a Bayesian approach, we specified priors and constructed posterior distributions as the basis for inference. The sampling results and diagnostics indicate that the chains satisfy the convergence criteria. The posterior means for the three skew normal parameters are 12.05, 1.04, and 2.26, corresponding to the location, scale, and shape parameters, respectively. Lastly, this study not only relaxes the assumption of normality but also provides a modeling foundation for Padua temperature data that can support prediction, forecasting, mapping, and other advanced data analysis purposes.
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