structured mean field
The naive mean field approximation assumes a fully factorized approximating distribution, which can be inaccurate if variables are tightly coupled. Structured mean field instead assumes the distribution factorizes into a product of tractable distributions, such as trees or chains.
This concept has the prerequisites:
- mean field approximation
- inference in MRFs (Structured mean field is a graphical model inference algorithm.)
- sum-product on trees (Structured mean field uses exact inference algorithms for graphical models, most commonly sum-product.)
Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)
Supplemental resources (the following are optional, but you may find them useful)
→ Graphical models, exponential families, and variational inference (2008)
An in-depth review of exact and approximate inference methods for graphical models.
Location: Section 5.5, pages 142-147
→ Probabilistic Graphical Models: Principles and Techniques
A very comprehensive textbook for a graduate-level course on probabilistic AI.
Location: Section 11.5.2, pages 456-468
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Section 21.4, pages 739-742
- hidden Markov models
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