
Inference in Graded Bayesian Networks
Machine learning provides algorithms that can learn from data and make i...
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A Discovery Algorithm for Directed Cyclis Graphs
Directed acyclic graphs have been used fruitfully to represent causal st...
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A Prior Distribution over Directed Acyclic Graphs for Sparse Bayesian Networks
The main contribution of this article is a new prior distribution over d...
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An Improved Admissible Heuristic for Learning Optimal Bayesian Networks
Recently two search algorithms, A* and breadthfirst branch and bound (B...
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Beyond DAGs: Modeling Causal Feedback with Fuzzy Cognitive Maps
Fuzzy cognitive maps (FCMs) model feedback causal relations in interwove...
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Probabilistic Structural Controllability in Causal Bayesian Networks
Humans routinely confront the following key question which could be view...
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Independence, Conditionality and Structure of DempsterShafer Belief Functions
Several approaches of structuring (factorization, decomposition) of Demp...
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A Probabilistic Network of Predicates
Bayesian networks are directed acyclic graphs representing independence relationships among a set of random variables. A random variable can be regarded as a set of exhaustive and mutually exclusive propositions. We argue that there are several drawbacks resulting from the propositional nature and acyclic structure of Bayesian networks. To remedy these shortcomings, we propose a probabilistic network where nodes represent unary predicates and which may contain directed cycles. The proposed representation allows us to represent domain knowledge in a single static network even though we cannot determine the instantiations of the predicates before hand. The ability to deal with cycles also enables us to handle cyclic causal tendencies and to recognize recursive plans.
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