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<p>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.</p>
belief networks; decision theory; information value; probability; approximate nonmyopic computation; belief maintenance; decision theory; information theory; probability

E. Horvitz, B. Middleton and D. Heckerman, "An Approximate Nonmyopic Computation for Value of Information," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 15, no. , pp. 292-298, 1993.
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