Re: Gibbs sampling and annealing (was: Help! Book recommendations?)

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#637

From: Charles Geyer
Date: Sun, 04 Oct 1992 19:22

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In article <1992Oct3.210337.21498@news2.cis.umn.edu> I wrote:

   It makes no sense to compare Gibbs sampling versus simulated annealing, 
   for example.

In article <92Oct4.130952edt.243@neuron.ai.toronto.edu> radford@cs.toronto.edu
(Radford Neal) replies:

> Correct, but not for the reasons you perhaps mean. Simulated annealing
> is a technique that can be used in conjunction with Gibbs sampling 
> (or other Markov chain methods, such as the Metropolis algorithm) 
> in order to reach the final equilibrium distribution more quickly.

Depends on how you think about Markov chain Monte Carlo.  The
Metropolis-Hastings algorithm produces a Markov chain with a known
stationary distribution.  This "annealing trick" doesn't.  So far
from mixing MCMC and simulated annealing, it is not MCMC at all.

Are there any practically useful convergence results for such schemes
once Markov chain theory is thrown out the window?

-- 
Charles Geyer
School of Statistics
University of Minnesota
charlie@umnstat.stat.umn.edu

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From: charlie@umnstat.stat.umn.edu (Charles Geyer)
Subject: Re: Gibbs sampling and annealing (was: Help!  Book recommendations?)
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