Maximum Likelihood Estimation (MLE) with Examples

This video introduces Maximum Likelihood Estimation (MLE), one of the most important methods in statistical parameter estimation. MLE is the basis of the Bayesian extension, maximum a posteriori (MAP) estimation.

This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company

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00:00 Intro
01:05 Problem Statement of MLE
05:58 Deriving the Estimator
12:20 Example: MLE of a Poisson
19:00 Recap
21:45 Note on Prior Knowledge & Outro