Independence diagrams are a graphical way of expressing the conditional independence relationships among a set of random variables. They cannot encode every possible form of conditional independence but they go a long way toward this end. They are also called ``Bayesian networks,'' which unfortunately suggests inappropriate comparisons to neural networks. This paper discusses how to read and write independence diagrams. The presentation is based on Pearl (1988).