A Survey on Latent Tree Models and Applications

Abstract

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.

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• Distance-based species tree estimation: information-theoretic trade-off between number of loci and sequence length under the coalescent
• Towards Building Deep Networks with Bayesian Factor Graphs
• Latent Tree Models for Hierarchical Topic Detection
• The correlation space of Gaussian latent tree models and model selection without fitting
• Layered Synthesis of Latent Gaussian Trees
• Tree Structured Synthesis of Gaussian Trees