Theoretical Aspects for Bayesian Predictions Based on Three-Parameter Burr-XII Distribution and Its Applications in Climatic Data

Symmetry and asymmetry play vital roles in prediction. Symmetrical data, which follows a predictable pattern, is easier to predict compared asymmetrical lacks pattern. helps identify patterns within data that can be utilized predictive models, while aids identifying outliers or anomalies should considered the model. Among various factors associated with storms their impact on surface temperatures, wind speed stands out as significant factor. This paper focuses predicting by utilizing unified hybrid censoring from three-parameter Burr-XII distribution. Bayesian prediction bounds for future observations are obtained using both one-sample two-sample techniques. As explicit expressions predictions of one two samples unavailable, we propose use Gibbs sampling process Markov chain Monte Carlo framework obtain estimated distributions. Furthermore, present climatic application demonstrate developed uncertainty procedures. Additionally, simulation research carried examine contrast effectiveness suggested methods. The results reveal Bayes estimates parameters outperformed Maximum likelihood estimators.