A computation method in robust Bayesian decision theory Original Research Article

We propose a method for computing the range of the optimal decisions when the utility function runs through a class image. The class image has constraints on the values and the shape of the utility functions. A discretization method enables to easily approximate the optimal decision associated with a particular utility function image. The range of optimal decisions is computed by a Monte Carlo optimization method. An example is provided with numerical results.