4 citations · 8 across the 5 of their papers we have counts for
3 papers · 1 filter
Bayesian Graphs of Intelligent Causation
Preetha Ramiah, James Q. Smith, Oliver Bunnin +3
Probabilistic Graphical Bayesian models of causation have continued to impact on strategic analyses designed to help evaluate the efficacy of different interventions on systems. Ho…
Propagating moments in probabilistic graphical models with polynomial regression forms for decision support systems
Victoria Volodina, Nikki Sonenberg, Peter Challenor +1
Probabilistic graphical models are widely used to model complex systems under uncertainty. Traditionally, Gaussian directed graphical models are applied for analysis of large netwo…
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…