# Maximum Likelihood Estimation (MLE) In many learning scenarios, the learner considers some set of candidate hypotheses $H$ and is interested in finding the most probable hypothesis $h \in H$ This maximally probable hypothesis is called a **maximum a posteriori (MAP) hypothesis** - [ ] Maximum Likelihood Estimation (MLE) ## Likelihood Functions A **likelihood function** measures how well a statistical model, specifically its associated parameters, explains an observed dataset. If a likelihood function is differentiable, the derivative test can be used to identify maxima - [ ] How do these differ from accuracy/sensitivity functions used to measure the performance of ML models? https://en.wikipedia.org/wiki/Maximum_likelihood_estimation