Introduction to Statistics and Data Analysis - Bayesian Maximum Aposteriori Estimation (MAP): Extending Maximum Likelihood Estimation

Maximum Aposteriori Estimation (MAP) is a Bayesian extension to the maximum likelihood estimate (MLE) to include prior information into the estimate. This is a major technique in distribution estimation, especially in applications where data is sparse and/or expensive, such as seismic inversion. This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company %%% CHAPTERS %%% 00:00 Intro 01:51 MLE Fragility wrt Bad Data 04:03 Applying a Prior with Bayes 07:45 Deriving a New Optimizer 09:51 Discussing the MAP & Outro