Görgen C, Leonelli M and Smith JQ
A differential approach for staged trees
Abstract: Symbolic inference algorithms in Bayesian networks have now been applied in a variety of domains. These often require the computation
of the derivatives of polynomials representing probabilities in such graphical models. In this paper we formalise a symbolic approach for
staged trees, a model class making it possible to visualise asymmetric model constraints. We are able to show that the probability parametrisation
associated to trees has several advantages over the one associated to Bayesian networks. We then continue to compute certain derivatives of
staged trees' polynomials and show their probabilistic interpretation. We are able to determine that these polynomials can be straightforwardly
deduced by compiling a tree into an arithmetic circuit.