Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
The EM algorithm for graphical association models with missing data
Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Using hidden nodes in Bayesian networks
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
A few logs suffice to build (almost) all trees: part II
Theoretical Computer Science
Human motion analysis: a review
Computer Vision and Image Understanding
Constructing Bayesian Networks to Predict Uncollectible Telecommunications Accounts
IEEE Expert: Intelligent Systems and Their Applications
Recognition of two-person interactions using a hierarchical Bayesian network
IWVS '03 First ACM SIGMM international workshop on Video surveillance
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
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
Latent tree models and diagnosis in traditional Chinese medicine
Artificial Intelligence in Medicine
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Clustering
Latent classification models for binary data
Pattern Recognition
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
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Search-based learning of latent tree models
Search-based learning of latent tree models
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
Large-scale neighbor-joining with NINJA
WABI'09 Proceedings of the 9th international conference on Algorithms in bioinformatics
Greedy Learning of Binary Latent Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Journal of Machine Learning Research
Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates
The Journal of Machine Learning Research
Learning Latent Tree Graphical Models
The Journal of Machine Learning Research
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
Learning the dimensionality of hidden variables
UAI'01 Proceedings of the Seventeenth 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
Learning hierarchical bayesian networks for large-scale data analysis
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Latent variable discovery in classification models
Artificial Intelligence in Medicine
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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In data analysis, latent variables play a central role because they help provide powerful insights into a wide variety of phenomena, ranging from biological to human sciences. The latent tree model, a particular type of probabilistic graphical models, deserves attention. Its simple structure - a tree - allows simple and efficient inference, while its latent variables capture complex relationships. In the past decade, the latent tree model has been subject to significant theoretical and methodological developments. In this review, we propose a comprehensive study of this model. First we summarize key ideas underlying the model. Second we explain how it can be efficiently learned from data. Third we illustrate its use within three types of applications: latent structure discovery, multidimensional clustering, and probabilistic inference. Finally, we conclude and give promising directions for future researches in this field.