MAP- and MLE-Based Teaching

Imagine a learner $L$ who tries to infer a hidden concept from a collection of observations. Building on the work of Ferri et al we assume the learner to be parameterized by priors $P(c)$ and by $c$-conditional likelihoods $P(z|c)$ where $c$ ranges over all concepts in a given class $C$ and $z$ ranges over all observations in an observation set $Z$. $L$ is called a MAP-learner (resp.~an MLE-learner) if it thinks of a collection $S$ of observations as a random …

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