EXPECTED UTILITY AND VARIANCE

 

This paper has so far concentrated on algorithms for posterior marginals. Most algorithms presented in this paper extend readily to expected utility by simple inclusion of the utility functions [10].

Calculation of lower and upper variances is more complex than expected utility because variances are non-linear functionals of the distributions. We must reduce calculation of variances to an iterative calculation of expected utilities [37, Theorem G2,] in order to solve this problem (the method is presented in [10]).