A General Framework for the Parametrization of Hierarchical Models

Details A General Framework for the Parametrization of Hierarchical Models A General Framework for the Parametrization of Hierarchical Models

2007-08-28

arxiv.org/abs/0708.3797v1

Statistical Science 2007, Vol. 22, No. 1, 59-73

Statistics

Methodology

Published at http://dx.doi.org/10.1214/088342307000000014 in the Statistical Science (http://www.imstat.org/sts/) by the Insti

Scientific paper

10.1214/088342307000000014

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.

Affiliated with

Also associated with

No associations

Rating

A General Framework for the Parametrization of Hierarchical Models does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with A General Framework for the Parametrization of Hierarchical Models, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A General Framework for the Parametrization of Hierarchical Models will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-363211