Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Causality and maximum entropy updating
International Journal of Approximate Reasoning
A note on the inevitability of maximum entropy
International Journal of Approximate Reasoning
Probabilistic Horn abduction and Bayesian networks
Artificial Intelligence
Anytime deduction for probabilistic logic
Artificial Intelligence
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Characterizing the principle of minimum cross-entropy within a conditional-logical framework
Artificial Intelligence
2U: an exact interval propagation algorithm for polytrees with binary variables
Artificial Intelligence
Combining Data and Knowledge by MaxEnt-Optimization of Probability Distributions
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
A Logically Sound Method for Uncertain Reasoning with Quantified Conditionals
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Probalilistic Logic Programming under Maximum Entropy
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Decision making with interval influence diagrams
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Irrelevance and Independence Axioms in Quasi-Bayesian Theory
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Random worlds and maximum entropy
Journal of Artificial Intelligence Research
Probabilistic deduction with conditional constraints over basic events
Journal of Artificial Intelligence Research
A method of computing generalized Bayesian probability values for expert systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Irrelevance and conditioning in first-order probabilistic logic
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Irrelevance and independence relations in Quasi-Bayesian networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Robustness analysis of Bayesian networks with local convex sets of distributions
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Coherent knowledge processing at maximum entropy by spirit
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Completing incomplete bayesian networks
WCII'02 Proceedings of the 2002 international conference on Conditionals, Information, and Inference
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We apply the principle of maximum entropy to select a unique joint probability distribution from the set of all joint probability distributions specified by a credal network. In detail, we start by showing that the unique joint distribution of a Bayesian tree coincides with the maximum entropy model of its conditional distributions. This result, however, does not hold anymore for general Bayesian networks. We thus present a new kind of maximum entropy models, which are computed sequentially. We then show that for all general Bayesian networks, the sequential maximum entropy model coincides with the unique joint distribution. Moreover, we apply the new principle of sequential maximum entropy to interval Bayesian networks and more generally to credal networks. We especially show that this application is equivalent to a number of small local entropy maximizations.