Mixture Models
===========================
Mixture models are used to infer properties about sub-populations when presented with pooled population data. Several applications such as topic modeling, clustering, and rich-prior distribtion selection can be handled with mixture models. *dmx-learn* offers a flexible implementation of the mixture model. *dmx-learn*'s mixture model API combined  with :doc:`base distributions <base_distributions>` and :doc:`combinators <combinators>`.` allows for the specification of deep nested graphical models on heterogenous data types. 


Mixture Models
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.. toctree::
   :maxdepth: 1

   /stats/mixture.rst
   /stats/heterogeneous_mixture.rst
   /stats/jmixture.rst
   /stats/hmixture.rst
   /stats/hidden_markov.rst