# 1997 AnIntroductionToGraphicalModels

- (Jordan, 1997) ⇒ Michael I. Jordan. (1997). “An Introduction to Graphical Models." Tutorial at NIPS-1997.

**Subject Headings:** Probabilistic Graphical Model.

## Notes

## Quotes

- 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),

Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
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1997 AnIntroductionToGraphicalModels | An Introduction to Graphical Models | http://www.cs.berkeley.edu/~jordan/papers/nips-handout.ps.Z |