Uploaded December 2025 | Updated September 2026, 2 weeks ago
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
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
![Neural Implicit Flow (NIF) [Physics Informed Machine Learning]](https://i.ytimg.com/vi/y-s1oECkbuU/mqdefault.jpg "Neural Implicit Flow (NIF) [Physics Informed Machine Learning]
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:25 Underlying Concept
// 02:32 Example Problem
04:36 Example Application: Turbulent Data Compression
06:23 Example Application: Sparse Sensor Placement
08:09 NIF is Mesh Agnostic
10:30 Results/Benchmark Data
// 11:00 Growing Vortices/ Cool Pictures
11:40 Shape Net Architectures
12:30 Outro")
