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It is silly to
run the Markov chain all the way to
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equilibrium if
we can get the information required
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for learning in
just a few steps.
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The
way in which the model systematically
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distorts
the data distribution in the first few
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steps
tells us a lot about how the model is
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wrong.
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But the
model could have strong modes far
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from
any data. These modes will not be
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sampled
by confabulations. Is this a problem
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in
practice?
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