Credal networks generalize Bayesian networks to allow for imprecision in probability values. This tutorial reviews the main results on credal networks, in particular under strong independence, as there has been significant progress in the literature during the last decade or so. We focus on computational aspects, summarizing the main algorithms and complexity results for inference and decision making. We address the question “What is really known about strong and epistemic extensions of credal networks?” by looking at theoretical results and by presenting a short summary of real applications.