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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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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 09 May 2021

Hierarchical modelling

Hierarchical modelling

(p.33) 3 Hierarchical modelling
Bayesian Theory and Applications

Alan E Gelfand

Souparno Ghosh

Oxford University Press

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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