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A general framework for the parametrization of hierarchical models

Papaspiliopoulos, Omiros; Roberts, Gareth O. and Sköld, Martin LU (2007) In Statistical Science 22(1). p.59-73
Abstract
In this paper, we describe centering and noncentering methodology as complementary techniques for use in parametrization of broad classes of hierarchical models, with a view to the construction of effective MCMC algorithms for exploring posterior distributions from these models. We give a clear qualitative understanding as to when centering and noncentering work well, and introduce theory concerning the convergence time complexity of Gibbs samplers using centered and noncentered parametrizations. We give general recipes for the construction of noncentered parametrizations, including an auxiliary variable technique called the state-space expansion technique. We also describe partially noncentered methods, and demonstrate their use in... (More)
In this paper, we describe centering and noncentering methodology as complementary techniques for use in parametrization of broad classes of hierarchical models, with a view to the construction of effective MCMC algorithms for exploring posterior distributions from these models. We give a clear qualitative understanding as to when centering and noncentering work well, and introduce theory concerning the convergence time complexity of Gibbs samplers using centered and noncentered parametrizations. We give general recipes for the construction of noncentered parametrizations, including an auxiliary variable technique called the state-space expansion technique. We also describe partially noncentered methods, and demonstrate their use in constructing robust Gibbs sampler algorithms whose convergence properties are not overly sensitive to the data. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
latent stochastic processes, parametrization, hierarchical models, MCMC
in
Statistical Science
volume
22
issue
1
pages
59 - 73
publisher
IMS
external identifiers
  • wos:000249036700007
  • scopus:34249101736
ISSN
0883-4237
DOI
10.1214/088342307000000014
language
English
LU publication?
yes
id
c9c02cec-8ecc-4d00-8646-4ad43806762f (old id 688610)
date added to LUP
2007-12-19 12:16:48
date last changed
2017-11-19 04:12:03
@article{c9c02cec-8ecc-4d00-8646-4ad43806762f,
  abstract     = {In this paper, we describe centering and noncentering methodology as complementary techniques for use in parametrization of broad classes of hierarchical models, with a view to the construction of effective MCMC algorithms for exploring posterior distributions from these models. We give a clear qualitative understanding as to when centering and noncentering work well, and introduce theory concerning the convergence time complexity of Gibbs samplers using centered and noncentered parametrizations. We give general recipes for the construction of noncentered parametrizations, including an auxiliary variable technique called the state-space expansion technique. We also describe partially noncentered methods, and demonstrate their use in constructing robust Gibbs sampler algorithms whose convergence properties are not overly sensitive to the data.},
  author       = {Papaspiliopoulos, Omiros and Roberts, Gareth O. and Sköld, Martin},
  issn         = {0883-4237},
  keyword      = {latent stochastic processes,parametrization,hierarchical models,MCMC},
  language     = {eng},
  number       = {1},
  pages        = {59--73},
  publisher    = {IMS},
  series       = {Statistical Science},
  title        = {A general framework for the parametrization of hierarchical models},
  url          = {http://dx.doi.org/10.1214/088342307000000014},
  volume       = {22},
  year         = {2007},
}