1997 AnIntroductionToGraphicalModels

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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),

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
1997 AnIntroductionToGraphicalModelsAn Introduction to Graphical Modelshttp://www.cs.berkeley.edu/~jordan/papers/nips-handout.ps.Z