We can now make a first connection between probability and graph theory. A belief network introduces structure into a probabilistic model by using graphs to represent independence assumptions among the variables. Probability operations such as marginalising and conditioning then correspond to simple operations on the graph, and details about the model can be ‘read’ from the graph. There is also a benefit in terms of computational efficiency. Belief networks cannot capture all possible relations among variables. However, they are natural for representing ‘causal’ relations, and they are a part of the family of graphical models we study further in Chapter 4.
3.1 The benefits of structure
It’s tempting to think of feeding a mass ...