datamicroscopes.github.io

datamicroscopes: Bayesian nonparametric models in Python

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.

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datamicroscopes: Bayesian nonparametric models in Python | datamicroscopes.github.io Reviews

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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.

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7 api reference

8 dataviews

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10 microscopes.common random

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datamicroscopes: Bayesian nonparametric models in Python | datamicroscopes.github.io Reviews

https://datamicroscopes.github.io

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.

INTERNAL PAGES

datamicroscopes.github.io

1

Discovering structure in your data: an overview of clustering

http://datamicroscopes.github.io/intro.html

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.

2

API Reference

http://datamicroscopes.github.io/api.html

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.

3

Clustering the Enron e-mail corpus using the Infinite Relational Model

http://datamicroscopes.github.io/enron_email.html

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.

4

Count Data and Ordinal Data with the Gamma-Poisson Distribution

http://datamicroscopes.github.io/gamma_poisson.html

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.

5

Real Valued Data and the Normal Inverse-Wishart Distribution

http://datamicroscopes.github.io/niw.html

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.

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Four new Recurse Center residents - Blog - Recurse Center

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Check out Code Words. A quarterly publication from the Recurse Center community. Four new Recurse Center residents. We’re excited to announce that David Turner, Ranjit Bhatnagar, Stephen Tu and James Coglan will be joining us at the Recurse Center as residents. In the next few months! If you’d like to work with David, Ranjit, Stephen, James, or residents like them, apply to the Recurse Center. Keep an eye on our blog for more resident announcements! And last year published JavaScript Testing Recipes.

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datamicroscopes.github.io

datamicroscopes: Bayesian nonparametric models in Python

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.

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