Sequential Model Criticism in Probabilistic Expert Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Distributed Learning Algorithm for Bayesian Inference Networks
IEEE Transactions on Knowledge and Data Engineering
Learning Functional Dependency Networks Based on Genetic Programming
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Adapting Bayes network structures to non-stationary domains
International Journal of Approximate Reasoning
Landmark detection from mobile life log using a modular Bayesian network model
Expert Systems with Applications: An International Journal
iMMPC: a local search approach for incremental Bayesian network structure learning
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
Robust inference of bayesian networks using speciated evolution and ensemble
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Financial Data Modeling using a Hybrid Bayesian Network Structured Learning Algorithm
International Journal of Cognitive Informatics and Natural Intelligence
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A new approach to refining Bayesian network structures from new data is developed. Most previous work has only considered the refinement of the network's conditional probability parameters and has not addressed the issue of refining the network's structure. We tackle this problem by a machine learning approach based on a formalism known as the Minimum Description Length (MDL) principle. The MDL principle is well suited to this task since it can perform tradeoffs between the accuracy, simplicity, and closeness to the existent structure. Another salient feature of this refinement approach is the capability of refining a network structure using partially specified data. Moreover, a localization scheme is developed for efficient computation of the description lengths since direct evaluation involves exponential time resources.