Project Code - Nonparametric Bayes

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Documentation for package ‘nonparametric.bayes’ version 0.0.1

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cluster_datapoints Gibbs sampling for the Chinese Restaurant Process Implementation details can be found in the associated paper The algorithm stops at every 1000th iteration and prints the current cluster configuration.
generate_dirichlet_clusters Draws from a Dirichlet distribution and shows the clusters that were generated by this draw. Varying alpha, will put more or less mass in the first clusters compared to higher clusters (rhos).
generate_dirichlet_clusters_with_sampled_points Draws from a Dirichlet distribution and shows the clusters that were generated by this draw. Additionally, adds points to these clusters and shows which clusters are occupied
generate_split_data Generates a dataset used to exemplify clustering The cluster centers are set relatively far away to see how well the algorithm performs in simple scenarios
rdirichlet Generate a sample from a Dirichlet distirbution Using: https://en.wikipedia.org/wiki/Dirichlet_distribution#Random_number_generation
rDPM Sequentially generate draws from a Dirichlet process mixture model, by showing step by step the iterations taken. The plot is centered at 0, with x and y from -5 to 5. The mixture draws the centres for clusters from a Normal distribution with mean mu and standard deviation sigma_0 Additional to plotting the points, it also returns the points sampled.
rDPM_visual Sequentially generate draws from a Dirichlet process mixture model, by showing step by step the iterations taken. The plot is centered at 0, with x and y from -5 to 5. The mixture draws the centres for clusters from a Normal distribution with mean mu and standard deviation sigma_0