Brute Force Bayes Concept Learning
- Brute-Force MAP Learning algorithm:
- For each hypothesis $h$ in $H$, calculate the posterior probability
\[ P(h\,|\,D) = \frac{P(D\,|\,h) P(h)}{P(D)}\]
where $D = (d_1\ldots d_m)$ is the set of target values from the set of
examples $X = ((x_1,d_1) \cdots (x_m,d_m))$.
- Output the hypothesis $h_{MAP}$ with the highest posterior probability
\[h_{MAP} = \argmax_{h \in H} P(h\,|\,D)\]
- This can be very slow since it applies BT to every $h\in H$.
José M. Vidal
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