M08.2 CONNECT THE MECHANISM
Let several hidden sources explain the data
A 440 oz sack could have come from either packing line. Learn how a mixture model splits the credit, and how EM learns both packing lines without ever being told which filled which sack.
LESSON OVERVIEW14 min lesson
Lesson overview
A 440 oz sack could have come from either packing line. Learn how a mixture model splits the credit, and how EM learns both packing lines without ever being told which filled which sack.
What you’ll explore
- Mixture models assign observations probabilistically to latent components; expectation-maximization alternates responsibility estimates and parameter updates under a chosen model.
GO TO THE SOURCE
Original explanations, connected to the research.
An Introduction to Statistical Learning — authors’ materialsDeep Learning — probability and information theoryMaximum Likelihood from Incomplete Data via the EM Algorithm (Dempster, Laird & Rubin, 1977)scikit-learn — Gaussian mixture modelsSuggest a correction
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