Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Efficient noise-tolerant learning from statistical queries
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Learning and robust learning of product distributions
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
On the hardness of approximate reasoning
Artificial Intelligence
PALO: a probabilistic hill-climbing algorithm
Artificial Intelligence
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Building classifiers using Bayesian networks
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
On the sample complexity of learning Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
ACM Computing Surveys (CSUR)
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Learning Bayesian Belief Network Classifiers: Algorithms and System
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Recognizing Time Pressure and Cognitive Load on the Basis of Speech: An Experimental Study
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Eighteenth national conference on Artificial intelligence
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Machine Learning
Classification using Hierarchical Naïve Bayes models
Machine Learning
Extended Naive Bayes classifier for mixed data
Expert Systems with Applications: An International Journal
Learning to assign degrees of belief in relational domains
Machine Learning
Discriminative model selection for belief net structures
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
When do numbers really matter?
Journal of Artificial Intelligence Research
Leveraging data about users in general in the learning of individual user models
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Comparing Bayesian network classifiers
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Exploiting qualitative knowledge in the learning of conditional probabilities of Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
When do numbers really matter?
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Estimating well-performing bayesian networks using Bernoulli mixtures
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Bayesian error-bars for belief net inference
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
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A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for learning BNs, however, attempt to optimize another criterion (usually likelihood, possibly augmented with a regularizing term), which is independent of the distribution of queries that are posed. This paper takes the "performance criteria" seriously, and considers the challenge of computing the BN whose performance -- read "accuracy over the distribution of queries" -- is optimal. We show that many aspects of this learning task are more difficult than the corresponding subtasks in the standard model.