Computer-based probabilistic-network construction
Computer-based probabilistic-network construction
Connectionist learning of belief networks
Artificial Intelligence
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
Operations for learning with graphical models
Journal of Artificial Intelligence Research
A characterization of the dirichlet distribution with application to learning Bayesian networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
A Bayesian approach to learning causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Efficient approximations for the marginal likelihood of incomplete data given a Bayesian network
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Machine Learning - Special issue on learning with probabilistic representations
Efficient Approximations for the MarginalLikelihood of Bayesian Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
Statistical Themes and Lessons for Data Mining
Data Mining and Knowledge Discovery
Towards a More Efficient Evolutionary Induction of Bayesian Networks
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Bayesian networks for discrete multivariate data: an algebraic approach to inference
Journal of Multivariate Analysis
Fusion of domain knowledge with data for structural learning in object oriented domains
The Journal of Machine Learning Research
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Asymptotic Model Selection for Naive Bayesian Networks
The Journal of Machine Learning Research
Conditional independence and chain event graphs
Artificial Intelligence
Latent tree models and diagnosis in traditional Chinese medicine
Artificial Intelligence in Medicine
Effective dimensions of hierarchical latent class models
Journal of Artificial Intelligence Research
Effective dimensions of partially observed polytrees
International Journal of Approximate Reasoning
Model-based multidimensional clustering of categorical data
Artificial Intelligence
Dimension correction for hierarchical latent class models
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Asymptotic model selection for naive Bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A Bayesian method for causal modeling and discovery under selection
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Model criticism of Bayesian networks with latent variables
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Graphical models and exponential families
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
On the geometry of Bayesian graphical models with hidden variables
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Sequential update of Bayesian network structure
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Models and selection criteria for regression and classification
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Structure and parameter learning for causal independence and causal interaction models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Efficient approximations for the marginal likelihood of incomplete data given a Bayesian network
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Automated analytic asymptotic evaluation of the marginal likelihood for latent models
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
The role of operation granularity in search-based learning of latent tree models
JSAI-isAI'10 Proceedings of the 2010 international conference on New Frontiers in Artificial Intelligence
A survey on latent tree models and applications
Journal of Artificial Intelligence Research
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We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for tile marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large sampies of data. Tile standard BIC as well as our extension punishes the complexity of a model according to tile dimension of its parameters. We argue that the dimension of a Bayesian uetwork with hidden variables is tile rank of the Jacobian matrix of the transformation between the parameters of the network and the parameters of the observable variables. We compute the dimensions of several networks including the naive Bayes model with a hidden root node.