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- (Jordan, 1997) ⇒ Michael I. Jordan. (1997). “An Introduction to Graphical Models." Tutorial at NIPS-1997.
Subject Headings: Probabilistic Graphical Model.
- Graphical models are a marriage between graph theory and probability theory
- They clarify the relationship between neural networks and related network-based models such as HMMs, MRFs, and Kalman lters
- Indeed, they can be used to give a fully probabilistic interpretation to many neural network architectures
- Some advantages of the graphical model point of view
- inference and learning are treated together
- supervised and unsupervised learning are merged seamlessly
- missing data handled nicely
- a focus on conditional independence and computational issues
- interpretability (if desired),
|1997 AnIntroductionToGraphicalModels||An Introduction to Graphical Models||http://www.cs.berkeley.edu/~jordan/papers/nips-handout.ps.Z|