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To learn W, we need the posterior
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distribution in
the first hidden layer.
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Problem
1: The posterior is typically
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complicated
because of “explaining
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away”.
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Problem
2: The posterior depends
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on the prior as
well as the likelihood.
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So
to learn W, we need to know
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the
weights in higher layers, even
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if
we are only approximating the
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posterior.
All the weights interact.
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Problem
3: We need to integrate
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over all possible
configurations of
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the higher
variables to get the prior
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for first hidden
layer. Yuk!
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