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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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Bayesian approaches to copula modelling

Bayesian approaches to copula modelling

Chapter:
(p.336) 17 Bayesian approaches to copula modelling
Source:
Bayesian Theory and Applications
Author(s):

Michael Stanley Smith

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0017

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.

Keywords:   Bayesian inference, copula models, continuous data, discrete data

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