Learning Causal Graphical Models with Latent Variables

Posted in Science on August 22, 2008


Learning Causal Graphical Models with Latent Variables

Lecture slides:

  • Learning Causal Graphical Models with Latent Variables
  • Introduction
  • Problem
  • Overview pt 3
  • Bayesian Networks (BN)
  • Causal Bayesian networks (CBN)
  • Modeling Latent Variables
  • Probabilistic vs Causal Inference
  • With latent variables
  • Overview pt 4
  • Our assumptions
  • Representation for causal inference
  • Modeling Latent Variables 1
  • Representation for causal inference 1
  • Inference in SMCMs
  • Representation for learning
  • Maximal Ancestral Graphs (MAG)
  • Learning from Observational Data
  • Markov Equivalence Class
  • Uncertainty in CPAGs
  • Inference in MAGs
  • Uncertainty in CPAGs 1
  • Inference in MAGs 1
  • Overview pt 5
  • CPAG - SMCM
  • CPAG - SMCM (Type 1)
  • Uncertainty in CPAGs 2
  • CPAG - SMCM (Type 1) 1
  • Uncertainty in CPAGs 3
  • CPAG - SMCM (Type 1) 2
  • CPAG - SMCM (Type 2)
  • CPAG - SMCM (ctd.)
  • Conclusion

Author: Sam Maes, Université De Savoie

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Tags: Science, Lectures, Computer Science, Machine Learning, VideoLectures.Net, Graphical Models