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Credit goes to datamicroscopes.github.io
datamicroscopes
0.1
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
Tutorials
Datatypes and likelihood models in datamicroscopes
Examples
Source
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
Datamicroscopes is developed by
Qadium
, with funding from the
DARPA
XDATA
program. Copyright Qadium 2015.