PRACTICAL BAYESIAN DENSITY ESTIMATION USING MIXTURES OF NORMALS
Kathryn Roeder and Larry Wasserman
Mixtures of normals provide a flexible model for estimating densities
in a Bayesian framework. But there are some difficulties with this
model. First, standard reference priors yield improper posteriors;
second, the posterior for the number of components in the mixture is
not well defined (if the reference prior is used) and third, posterior
simulation does not provide a direct estimate of the posterior for the
number of components. We present some practical methods for coping
with these problems. Finally we give some results on the consistency
of the method when the maximum number of components is allowed to grow
with the sample size.
Keywords: Markov chain Monte Carlo, Normal mixture, Partially proper
Here is the full postscript text for this
technical report. It is 684785 bytes long.