Introduction to Statistics and Data Analysis - 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 %%% CHAPTERS %%% 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