A Bayesian Copula Approach for Flood Analysis





Bayesian Analysis, Copula, MCMC


This study aims to provide joint modelling of rainfall characteristics in Peninsular Malaysia using two-dimensional copula. Two commonly regarded as important variables in the field of hydrology, namely rainfall severity and duration were derived using the Standard Precipitation Index (SPI) and their univariate marginal distributions are further identified by fitting into several distributions. The paper uses a Bayesian framework to estimate the parameter values in the marginal and copula model. The approximation of the posterior distribution by random sampling has been done by Monte Carlo Markov Chain (MCMC). Next, the authors compared these findings with those based on the classical procedure. The results indicated that the Bayesian approach can be substantially more reliable in parameter estimation for small samples.


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