@Article{heckerman93a,
  author =	 {David Heckerman and Eric Horvitz and Blackford
                  Middleton},
  title =	 {An approximate nonmyopic computation for value of
                  information},
  journal =	 {{IEEE} Transactions on Pattern Analysis and Machine
                  Intelligence},
  year =	 1993,
  volume =	 13,
  number =	 3,
  pages =	 {292--299},
  abstract =	 {It is argued that decision analysts and
                  expert-system designers have avoided the
                  intractability of exact computation of the value of
                  information by relying on a myopic assumption that
                  only one additional test will be performed, even
                  when there is an opportunity to make large number of
                  observations. An alternative to the myopic analysis
                  is presented. In particular, an approximate method
                  for computing the value of information of a set of
                  tests, which exploits the statistical properties of
                  large samples, is given. The approximation is linear
                  in the number of tests, in contrast with the exact
                  computation, which is exponential in the number of
                  tests. The approach is not as general as in a
                  complete nonmyopic analysis, in which all possible
                  sequences of observations are considered. In
                  addition, the approximation is limited to specific
                  classes of dependencies among evidence and to binary
                  hypothesis and decision variables. Nonetheless, as
                  demonstrated with a simple application, the approach
                  can offer an improvement over the myopic analysis.},
  url = 	 {http://jmvidal.cse.sc.edu/library/heckerman93a.pdf},
  doi = 	 {10.1109/34.204912},
  cluster = 	 {16471335311131664838}
}