• GitHub
• Qadium
• Site
  • Discovering structure in your data: an overview of clustering
  • Finding the number of clusters with the Dirichlet Process
  • Network Modeling with the Infinite Relational Model
  • Bayesian Nonparametric Topic Modeling with the Daily Kos
  • 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
  • Inferring Gaussians with the Dirichlet Process Mixture Model
  • Digit recognition with the MNIST dataset
  • Clustering the Enron e-mail corpus using the Infinite Relational Model
  • Learning Topics in The Daily Kos with the Hierarchical Dirichlet Process
  • Tutorials
    • Discovering structure in your data: an overview of clustering
    • Finding the number of clusters with the Dirichlet Process
    • Network Modeling with the Infinite Relational Model
    • Bayesian Nonparametric Topic Modeling with the Daily Kos
  • Datatypes and likelihood models in datamicroscopes
    • 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
  • Examples
    • Inferring Gaussians with the Dirichlet Process Mixture Model
    • Digit recognition with the MNIST dataset
    • Clustering the Enron e-mail corpus using the Infinite Relational Model
    • Learning Topics in The Daily Kos with the Hierarchical Dirichlet Process
  • API Reference
    • dataviews
    • util
    • microscopes.common.random
    • query
    • microscopes.common.validator
    • parallel
    • mixturemodel
    • irm
    • kernels
    • Indices and tables
• Contents