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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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Hierarchical modelling

Hierarchical modelling

Chapter:
(p.33) 3 Hierarchical modelling
Source:
Bayesian Theory and Applications
Author(s):

Alan E Gelfand

Souparno Ghosh

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0003

This chapter reviews the range of hierarchical modelling. It argues that hierarchical models provide the stochastic framework within which to develop integrative process models. The chapter is organized as follows. Section 3.2 recalls the basics of hierarchical forms, including random effects and missing data. Section 3.3 offers some scope for the introduction of other sorts of latent variables. Section 3.4 considers mixture models while Section 3.5 returns to random effects, primarily in the context of structured dependence. Section 3.6 looks at dynamic models while Section 3.7 considers relatively recent ideas in data fusion. The chapter ends with a brief summary in Section 3.8.

Keywords:   hierarchical forms, random effects, missing data, mixture models, dynamic models, data fusion, integrative process models

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