@Article{henry87a,
  author =	 {Henry E. Kyburg, Jr.},
  title =	 {Bayesian and non-bayesian evidential updating},
  googleid = 	 {4avD0ezDNBoJ:scholar.google.com/},
  journal =	 {Artificial Intelligence},
  year =	 1987,
  volume =	 31,
  number =	 3,
  pages =	 {271--293},
  abstract =	 {Four main results are arrived at in this paper. (1)
                  Closed convex sets of classical probability
                  functions provide a representation of belief that
                  includes the representations provided by Shafer
                  probability mass functions as a special case. (2)
                  The impact of "uncertain evidence" can be (formally)
                  represented by Dempster conditioning, in Shafer's
                  framework. (3) The impact of "uncertain evidence"
                  can be (formally) represented in the framework of
                  convex sets of classical probabilities by classical
                  conditionalization. (4) The probability intervals
                  that result from Dempster-Shafer updating on
                  uncertain evidence are included in (and may be
                  properly included in) the intervals that result from
                  Bayesian updating on uncertain evidence.},
  keywords = 	 {ai bayesian},
  cluster = 	 {1888349565674040289}
}