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Allow each of
the 6 weights or biases to have
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the 9 possible
values [-2 : 0.5 : 2]
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So
there are 9^6 grid-points in parameter
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space.
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For each
grid-point compute the probability of
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the observed
outputs of all the training cases.
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This
is the likelihood term and is explained
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on
the next slide
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Multiply the
prior for each grid-point by the
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likelihood term
and renormalize to get the
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posterior
probability for each grid-point.
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Make predictions
by using the posterior
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probabilities to
average the predictions made by
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the different
grid-points.
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