1997 AnIntroductionToGraphicalModels

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

Subject Headings: Probabilistic Graphical Model.

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
1997 AnIntroductionToGraphicalModels				An Introduction to Graphical Models			http://www.cs.berkeley.edu/~jordan/papers/nips-handout.ps.Z