  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.
