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Multivariate Copula Analysis Toolbox (Mvcat): Describing Dependence And Underlying Uncertainty Using A Bayesian Framework, Mojtaba Sadegh, Elisa Ragno, Amir Aghakouchak

Civil Engineering Faculty Publications and Presentations

We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual-based Gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties. The contribution of this paper is threefold: (a) providing a Bayesian framework to approximate the predictive uncertainties of fitted copulas, (b) introducing a hybrid-evolution Markov Chain Monte Carlo (MCMC) approach designed for numerical estimation of the posterior distribution of copula parameters, and (c) enabling the community to explore a wide range of copulas and evaluate them relative …