Bayesian approaches to copula modelling

Abstract

This chapter introduces contemporary copula modelling to Bayesian statisticians and presents the advantages of Bayesian inference when applied to copula models. It begins with an introduction to copula modelling. It then describes what makes copula models so useful, how copulas models can be viewed as transformations, what are copulas constructed by inversion and vine copulas, and why the D-vine copula is an attractive model of serial dependence. This is followed by discussions of approaches to formulating and estimating copula models for multivariate continuous and discrete data.

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