Bayesian extreme rainfall analysis using informative prior: A case study of alor setar

Abstract

Bayesian analysis is an alternative approach in statistical inferences. The inclusion of other information regarding the parameter of the model is one of analysis capabilities. In the area of extreme rainfall analysis, expert opinion can be used as prior information to model the extreme events. Thus, considering previous or expert knowledge about the parameter of interest would reduce the uncertainty of the model. In this study, the annual maximum (AM) rainfall data of Alor Setar rain gauge station is modeled by the Generalized Extreme Value (GEV) distribution. A Bayesian Markov Chain Monte Carlo (MCMC) simulation is used for parameter estimation. Comparison of the outcomes between non-informative and informative priors is our main interest. The results show that there is a reduction in estimated values, which is due to informative priors.