Dear List Members,
I know that doing MCMC on mixture models
has the multi-modality issue due to permutation of labels.
But is this a common issue in models
that are not exactly mixtures? Such as those
models with multiple layers of random variables,
resulting in a non-convex posterior density surface.
If so, then what additional care do we commonly
need when making estimations from the samples
in Gibbs sampling? For mixtures we have various
ways to deal with label switching, but for a general
model with multi-modality, do we simply estimate
parameters by averaging the samples? Or is there
anyway to restrict the joint samples to be within
a major posterior density area?
Thanks!
Louis
--
Louis Yuanlong Shao
Department of Computer Science and Engineering
Ohio State University
http://www.shaoyuanlong.com
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