datamicroscopes is a library for discovering structure in your data. It implements several Bayesian nonparametric models for clustering such as the Dirichlet Process Mixture Model (DPMM) , the Infinite Relational Model (IRM) , and the Hierarchichal Dirichlet Process (HDP) . These models rely on the Dirichlet Process, which allow for the automatic learning of the number of clusters in a datset. Additionally, our API provides users with a flexible set of likelihood models for various types of data, such as binary, ordinal, categorical, and real-valued variables( datatypes) .

Please read our introduction for an overview of clustering and structure discovery.

- Datatypes and Bayesian Nonparametric Models
- Binary Data with the Beta Bernouli Distribution
- Categorical Data and the Dirichlet Discrete Distribution
- Real Valued Data and the Normal Inverse-Wishart Distribution
- Univariate Data with the Normal Inverse Chi-Square Distribution
- Count Data and Ordinal Data with the Gamma-Poisson Distribution