Fusion, propagation, and structuring in belief networks
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Boolean Feature Discovery in Empirical Learning
Machine Learning
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Introduction to Algorithms: A Creative Approach
Introduction to Algorithms: A Creative Approach
Uncertain Information Processing in Expert Systems
Uncertain Information Processing in Expert Systems
Information Theory and Reliable Communication
Information Theory and Reliable Communication
Representation of Bayesian Networks as Relational Databases
IPMU'94 Selected papers from the 5th International Conference on Processing and Management of Uncertainty in Knowledge-Based Systems, Advances in Intelligent Computing
an entropy-driven system for construction of probabilistic expert systems from databases
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
Algorithmic Graph Theory and Perfect Graphs (Annals of Discrete Mathematics, Vol 57)
A method for implementing a probabilistic model as a relational database
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Parallel Learning of Belief Networks in Large and Difficult Domains
Data Mining and Knowledge Discovery
A Study of Conditional Independence Change in Learning Probabilistic Network
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Learning Pseudo-independent Models: Analytical and Experimental Results
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Local Score Computation in Learning Belief Networks
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Reasoning and learning in extended structured Bayesian networks
Fundamenta Informaticae
Very large Bayesian multinets for text classification
Future Generation Computer Systems
Approximate factorizations of distributions and the minimum relative entropy principle
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
The Estimation of Distributions and the Minimum Relative Entropy Principle
Evolutionary Computation
The minimum-entropy set cover problem
Theoretical Computer Science - Automata, languages and programming: Algorithms and complexity (ICALP-A 2004)
Complexity measurement of fundamental pseudo-independent models
International Journal of Approximate Reasoning
Very large Bayesian multinets for text classification
Future Generation Computer Systems
Exploring parallelism in learning belief networks
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Learning belief networks in domains with recursively embedded pseudo independent submodels
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Foundation for the new algorithm learning pseudo-independent models
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Reasoning and Learning in Extended Structured Bayesian Networks
Fundamenta Informaticae
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Several scoring metrics are used in different search procedures for learning probabilistic networks. We study the properties of cross entropy in learning a decomposable Markov network. Though entropy and related scoring metrics were widely used, its “microscopic” properties and asymptotic behavior in a search have not been analyzed. We present such a “microscopic” study of a minimum entropy search algorithm, and show that it learns an I-map of the domain model when the data size is large.Search procedures that modify a network structure one link at a time have been commonly used for efficiency. Our study indicates that a class of domain models cannot be learned by such procedures. This suggests that prior knowledge about the problem domain together with a multi-link search strategy would provide an effective way to uncover many domain models.