IDs are probabilistic graphical models used to represent and solve decision problems under uncertainty. Interval-valued IDs allow to gain realism in the modeling and perform a sensitivity analysis in precise IDs. The variable elimination and arc reversal inference algorithms are generalized to evaluate interval-valued IDs. The experimental work shows a better accuracy of the new methods based on linear programming.