| Abstract: | As global or combinatorial optimization problems are not effectively tractable by means of deterministic techniques, Monte Carlo methods are used in practice for obtaining ”good“ approximations to the optimum. In order to test the accuracy achieved after a sample of finite size, the Bayesian nonparametric approach is proposed as a suitable context, and the theoretical as well as computational implications of prior distributions in the class of neutral to the right distributions are examined. The feasibility of the approach relatively to particular Monte Carlo procedures is finally illustrated both for the global optimization problem and the {0 - 1} programming problem. |
