Bayesian Modelling of Summer Daily Maximum Temperature Data

Abstract

The extremes of summer daily maximum temperature was analyzed using the generalized Pareto distribution (GPD) to the Bisho weather station data, Eastern Cape Province, South Africa. Since the extreme events are naturally scarce it is expected that the use of a Bayesian inference may improve the efficiency of the parameters estimates of the distribution compared to the maximum likelihood method. Therefore, the Bayesian approach was also applied in the paper using the Markov Chain Monte Carlo for the generalized Pareto distribution. The expected improvement in efficiency is not fully achieved in this study using the noninformative and informative priors. However, the effects of informative prior constructed from historical data depends on the distance.