| Bayes rule |
| In terms of our problem: |
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| what
we measure |
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| (likelihood) |
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| domain
knowledge |
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| (prior) |
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| what
we want |
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| (posterior) |
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| normalization
term |
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| The prior: P(skin) |
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| • | Could use domain
knowledge |
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| – | P(skin) may be
larger if we know the image contains a person |
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| – | for a portrait,
P(skin) may be higher for pixels in the center |
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| • | Could learn the
prior from the training set. How? |
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| – | P(skin) may be proportion
of skin pixels in training set |