Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
The Art of Causal Conjecture
Probabilistic decision graphs-combining verification and AI techniques for probabilistic inference
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Case-factor diagrams for structured probabilistic modeling
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Probabilistic Conditional Independence Structures: With 42 Illustrations (Information Science and Statistics)
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Causal analysis with Chain Event Graphs
Artificial Intelligence
Bayesian MAP model selection of chain event graphs
Journal of Multivariate Analysis
Formulating Asymmetric Decision Problems as Decision Circuits
Decision Analysis
Learning recursive probability trees from probabilistic potentials
International Journal of Approximate Reasoning
Causal identifiability via Chain Event Graphs
Artificial Intelligence
Refining a Bayesian Network using a Chain Event Graph
International Journal of Approximate Reasoning
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Graphs provide an excellent framework for interrogating symmetric models of measurement random variables and discovering their implied conditional independence structure. However, it is not unusual for a model to be specified from a description of how a process unfolds (i.e. via its event tree), rather than through relationships between a given set of measurements. Here we introduce a new mixed graphical structure called the chain event graph that is a function of this event tree and a set of elicited equivalence relationships. This graph is more expressive and flexible than either the Bayesian network-equivalent in the symmetric case-or the probability decision graph. Various separation theorems are proved for the chain event graph. These enable implied conditional independencies to be read from the graph's topology. We also show how the topology can be exploited to tease out the interesting conditional independence structure of functions of random variables associated with the underlying event tree.