most citedThe causal manipulation of chain event graphs

4 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ME20082 cited

Minimal average degree aberration and the state polytope for experimental designs

Yael Berstein, Hugo Maruri-Aguilar, Shmuel Onn +2

For a particular experimental design, there is interest in finding which polynomial models can be identified in the usual regression set up. The algebraic methods based on Groebner…

stat.ME20074 cited

The causal manipulation of chain event graphs

Eva Riccomagno, Jim Q. Smith

Discrete Bayesian Networks have been very successful as a framework both for inference and for expressing certain causal hypotheses. In this paper we present a class of graphical m…

stat.ME20073 cited

Algebraic causality: Bayes nets and beyond

Eva Riccomagno, Jim Q Smith

The relationship between algebraic geometry and the inferential framework of the Bayesian Networks with hidden variables has now been fruitfully explored and exploited by a number…

stat.ME20071 cited

Two polynomial representations of experimental design

Roberto Notari, Eva Riccomagno, Maria-Piera Rogantin

In the context of algebraic statistics an experimental design is described by a set of polynomials called the design ideal. This, in turn, is generated by finite sets of polynomial…

math.CO2007

Nonlinear Matroid Optimization and Experimental Design

Yael Berstein, Jon Lee, Hugo Maruri-Aguilar +4

We study the problem of optimizing nonlinear objective functions over matroids presented by oracles or explicitly. Such functions can be interpreted as the balancing of multi-crite…