1Introduction to mixture models
1.1 Introduction
Mixture models, also known as latent class models and unsupervised learning models, have experienced increasing interest over the last few decades. They are natural models for a heterogeneous population that consists of several unobserved homogeneous sub-populations. The homogeneous sub-populations are often called components of the population. Mixture models can be used for cluster analysis, latent class analysis, discriminant analysis, image analysis, survival analysis, disease mapping, meta-analysis, etc. Mixture models provide extremely flexible descriptive models for distributions in data analysis and inference, and are widely applied in many fields, including ...
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