Probabilistic Graphical Model Family

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A Probabilistic Graphical Model Family is a statistical model family that uses a graph structure to represent probability distributions between random variables.



References

2020

  1. Koller, D.; Friedman, N. (2009). Probabilistic Graphical Models. Massachusetts: MIT Press. p. 1208. ISBN 978-0-262-01319-2. Archived from the original on 2014-04-27.

2014

2009

2008

2006

2000

1999

1998a

1998b

1997a

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

1997b

1996

  • (Lauritzen, 1996) ⇒ S. Lauritzen. (1996). “Graphical Models.” Oxford.
    • mathematical exposition of the theory of graphical models.

1988