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).