Bayesian Inference for D-vines: Estimation and Model Selection

During the last two decades the advent of fast computers has made Bayesian inference based on Markov Chain Monte Carlo (MCMC) methods very popular in many fields of science. These Bayesian methods are good alternatives to traditional maximum likelihood (ML) methods since they often can estimate complicated statistical models for which a ML approach fails. In this paper we review available MCMC estimation and model selection algorithms as well as their possible extensions for a D-vine pair copula constructions (PCC) based on bivariate t-copulas. However the discussed methods can easily be extended for an arbitrary regular vine PCC based on any bivariate copulas. A Bayesian inference for Australian electricity loads demonstrates the addressed algorithms at work.     «

During the last two decades the advent of fast computers has made Bayesian inference based on Markov Chain Monte Carlo (MCMC) methods very popular in many fields of science. These Bayesian methods are good alternatives to traditional maximum likelihood (ML) methods since they often can estimate complicated statistical models for which a ML approach fails. In this paper we review available MCMC estimation and model selection algorithms as well as their possible extensions for a D-vine pair copula...     »