  Latent tree analysis seeks to model the correlations among a set of random
variables using a tree of latent variables. It was proposed as an improvement
to latent class analysis --- a method widely used in social sciences and
medicine to identify homogeneous subgroups in a population. It provides new and
fruitful perspectives on a number of machine learning areas, including cluster
analysis, topic detection, and deep probabilistic modeling. This paper gives an
overview of the research on latent tree analysis and various ways it is used in
practice.
