The algebra of causal trees and chain event graphs

One of the most flexible ways of representing families of discrete probability distributions is through event trees and the chain event graph. These are also extremely useful for representing causal hypotheses. In this talk I will show how the algebra associated with event tree families is less constrained and therefore more expressive than its Bayesian Network competitor. I will illustrate how an understanding of the algebraic features of these models gives insight into their underlying structure.