@InProceedings{shachter98a,
  author =	 {Ross D. Shachter},
  title =	 {Bayes-Ball: The Rational Pastime (for Determining
                  Irrelevance and Requisite Information in Belief
                  Networks and Influence Diagrams)},
  booktitle =	 {Proceedings of the Fourteenth Conference in
                  Uncertainty in Artificial Intelligence},
  pages =	 {480--487},
  year =	 1998,
  abstract =	 {One of the benefits of belief networks and influence
                  diagrams is that so much knowledge is captured in
                  the graphical structure. In particular, statements
                  of conditional irrelevance (or independence) can be
                  verified in time linear in the size of the graph. To
                  resolve a particular inference query or decision
                  problem, only some of the possible states and
                  probability distributions must be specified, the
                  ``requisite information''. This paper presents a
                  new, simple, and efficient ``Bayes-ball'' algorithm
                  which is wellsuited to both new students of belief
                  networks and state of the art implementations. The
                  Bayes-ball algorithm determines irrelevant sets and
                  requisite information more efficiently than existing
                  methods, and is linear in the size of the graph for
                  belief networks and influence diagrams.},
  url = 	 {http://jmvidal.cse.sc.edu/library/shachter98a.pdf},
  cluster = 	 {7719555913661701565},
  keywords = 	 {ai bayes}
}