Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Optimal structure identification with greedy search
The Journal of Machine Learning Research
Hierarchical Latent Class Models for Cluster Analysis
The Journal of Machine Learning Research
Efficient Learning of Hierarchical Latent Class Models
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Learning Hidden Variable Networks: The Information Bottleneck Approach
The Journal of Machine Learning Research
Latent tree models and diagnosis in traditional Chinese medicine
Artificial Intelligence in Medicine
Discovering Latent Structures: Experience with the CoIL Challenge 2000 Data Set
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007
Latent tree models and approximate inference in Bayesian networks
Journal of Artificial Intelligence Research
Search-based learning of latent tree models
Search-based learning of latent tree models
Asymptotic model selection for directed networks with hidden variables*
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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Latent tree (LT) models are a special class of Bayesian networks that can be used for cluster analysis, latent structure discovery and density estimation. A number of search-based algorithms for learning LT models have been developed. In particular, the HSHC algorithm by [1] and the EAST algorithm by [2] are able to deal with data sets with dozens to around 100 variables. Both HSHC and EAST aim at finding the LT model with the highest BIC score. However, they use another criterion called the cost-effectiveness principle when selecting among some of the candidate models during search. In this paper, we investigate whether and why this is necessary.