(1.7 hours to learn)


D-separation gives a way of determining conditional independence properties in Bayes nets in terms of the graph structure. It captures an intuitive notion of the "flow" of probabilistic influence through the graph.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Coursera: Probabilistic Graphical Models (2013)
An online course on probabilistic graphical models.
Author: Daphne Koller
Other notes:
  • Click on "Preview" to see the videos.


See also

  • The Markov blanket is another characterization of Bayes nets in terms of conditional independencies.
  • The d-separation criterion presented here is inefficient when implemented as an algorithm. The Bayes Ball algorithm gives an efficient way of testing conditional independence.