Neural Computation
A practical Bayesian framework for backpropagation networks
Neural Computation
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Score and information for recursive exponential models with incomplete data
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
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Context-specific Bayesian clustering for gene expression data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Accelerating EM for Large Databases
Machine Learning
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction
Machine Learning - Special issue: Unsupervised learning
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Model selection for probabilistic clustering using cross-validatedlikelihood
Statistics and Computing
An Experimental Comparison of Model-Based Clustering Methods
Machine Learning
A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections
Journal of Intelligent Information Systems
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling dependencies in protein-DNA binding sites
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
Temporal Pattern Generation Using Hidden Markov Model Based Unsupervised Classification
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Hierarchical latent class models for cluster analysis
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
Learning Bayesian Networks from Incomplete Data Based on EMI Method
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Learning Bayesian network classifiers by maximizing conditional likelihood
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Learning Hidden Variable Networks: The Information Bottleneck Approach
The Journal of Machine Learning Research
A Bayesian network based sequential inference for diagnosis of diseases from retinal images
Pattern Recognition Letters - Special issue: Advances in pattern recognition
Online Model Selection Based on the Variational Bayes
Neural Computation
Predicting dire outcomes of patients with community acquired pneumonia
Journal of Biomedical Informatics - Special issue: Clinical machine learning
Parametric model-based motion segmentation using surface selection criterion
Computer Vision and Image Understanding
Cost-sensitive feature acquisition and classification
Pattern Recognition
Incremental learning of cognitive concepts: a hidden variable networks approach
PCAR '06 Proceedings of the 2006 international symposium on Practical cognitive agents and robots
A Bayesian approach for structural learning with hidden Markov models
Scientific Programming - Hidden Markov Models
IEEE Transactions on Knowledge and Data Engineering
A comparative study of model selection criteria for computer vision applications
Image and Vision Computing
Boosted Bayesian network classifiers
Machine Learning
Learning Bayesian Networks Based on a Mutual Information Scoring Function and EMI Method
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Context-Specific Independence Mixture Modelling for Protein Families
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A Bayesian Approach to Attention Control and Concept Abstraction
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
Approximation Methods for Efficient Learning of Bayesian Networks
Proceedings of the 2008 conference on Approximation Methods for Efficient Learning of Bayesian Networks
Probability Density Estimation by Perturbing and Combining Tree Structured Markov Networks
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
COD: online temporal clustering for outbreak detection
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Latent tree models and approximate inference in Bayesian networks
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Effective dimensions of hierarchical latent class models
Journal of Artificial Intelligence Research
Latent tree models and approximate inference in Bayesian networks
Journal of Artificial Intelligence Research
Process-oriented estimation of generalization error
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Randomized maps for assessing the reliability of patients clusters in DNA microarray data analyses
Artificial Intelligence in Medicine
Integrating spatial and color information in images using a statistical framework
Expert Systems with Applications: An International Journal
Parametric model-based motion segmentation using surface selection criterion
Computer Vision and Image Understanding
Marginal Likelihood Integrals for Mixtures of Independence Models
The Journal of Machine Learning Research
Reconstructing the phylogeny of mobile elements
RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
Learning Bayesian networks with combination of MRMR criterion and EMI method
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A parallel algorithm for learning Bayesian networks
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Learning Bayesian network structure from incomplete data without any assumption
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling
IEEE Transactions on Neural Networks
Probabilistic graphical models in artificial intelligence
Applied Soft Computing
Bayesian multiscale smoothing in supervised and semi-supervised kernel discriminant analysis
Computational Statistics & Data Analysis
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Model-based multidimensional clustering of categorical data
Artificial Intelligence
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Fast learning from sparse data
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A Bayesian network classifier that combines a finite mixture model and a naïve bayes model
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Dimension correction for hierarchical latent class models
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Learning the dimensionality of hidden variables
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An experimental comparison of several clustering and initialization methods
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An Asymptotic Behaviour of the Marginal Likelihood for General Markov Models
The Journal of Machine Learning Research
An algorithm for cooperative learning of bayesian network structure from data
CSCWD'04 Proceedings of the 8th international conference on Computer Supported Cooperative Work in Design I
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
Expert Systems with Applications: An International Journal
Short communication: On estimating simple probabilistic discriminative models with subclasses
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
Issues and requirements for bayesian approaches in context aware systems
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
A probabilistic approach for semi-supervised nearest neighbor classification
Pattern Recognition Letters
Latent variable discovery in classification models
Artificial Intelligence in Medicine
A collaborative filtering recommendation system by unifying user similarity and item similarity
WAIM'11 Proceedings of the 2011 international conference on Web-Age Information Management
Computational aspects of fitting mixture models via the expectation-maximization algorithm
Computational Statistics & Data Analysis
Model-based clustering of high-dimensional data: Variable selection versus facet determination
International Journal of Approximate Reasoning
LTC: A latent tree approach to classification
International Journal of Approximate Reasoning
A survey on latent tree models and applications
Journal of Artificial Intelligence Research
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We discuss Bayesian methods for model averaging and modelselection among Bayesian-network models with hidden variables. Inparticular, we examine large-sample approximations for the marginallikelihood of naive-Bayes models in which the root node is hidden.Such models are useful for clustering or unsupervised learning. Weconsider a Laplace approximation and the less accurate but morecomputationally efficient approximation known as the BayesianInformation Criterion (BIC), which is equivalent to Rissanen‘s(1987) Minimum Description Length (MDL). Also, weconsider approximations that ignore some off-diagonal elements of theobserved information matrix and an approximation proposed by Cheesemanand Stutz (1995). We evaluate the accuracy of theseapproximations using a Monte-Carlo gold standard. In experiments withartificial and real examples, we find that (1) none of theapproximations are accurate when used for model averaging, (2) all ofthe approximations, with the exception of BIC/MDL, are accurate formodel selection, (3) among the accurate approximations, theCheeseman–Stutz and Diagonal approximations are the mostcomputationally efficient, (4) all of the approximations, with theexception of BIC/MDL, can be sensitive to the prior distribution overmodel parameters, and (5) the Cheeseman–Stutz approximation can bemore accurate than the other approximations, including the Laplaceapproximation, in situations where the parameters in the maximum aposteriori configuration are near a boundary.