Subjective bayesian methods for rule-based inference systems
AFIPS '76 Proceedings of the June 7-10, 1976, national computer conference and exposition
Criteria to evaluate approximate belief network representations in expert systems
Decision Support Systems
Incorporating default inferences into plan recognition
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
A maximum entropy approach to nonmonotonic reasoning
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Learning of expert systems from data
PKWBS-W'84 Proceedings of the 1984 IEEE conference on Principles of knowledge-based systems
Credal networks under maximum entropy
UAI'00 Proceedings of the Sixteenth 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
Additive belief-network models
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Integrating probabilistic rules into neural networks: a stochastic EM learning algorithm
UAI'91 Proceedings of the Seventh conference on Uncertainty in Artificial Intelligence
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This paper presents a new method for calculating the conditional probability of any multi-valued predicate given particular information about the individual case. This calculation is based on the principle of Maximum Entropy (ME), sometimes called the principle of least information, and gives the most unbiased probability estimate given the available evidence. Previous methods for computing maximum entropy values shows that they are either very restrictive in the probabilistic information (constraints) they can use or combinatorially explosive. The computational complexity of the new procedure depends on the inter-connectedness of the constraints, but in practical cases it is small. In addition, the maximum entropy method can give a measure of how accurately a calculated conditional probability is known.