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pyrichlet: A Python Package for Density Estimation and Clustering Using Gaussian Mixture Models
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DOI：10.18637/jss.v112.i08.png)
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Abstract
En 中文
Bayesian nonparametric models have proven to be successful tools for clustering and density estimation. While there exists a nourished ecosystem of implementations in R, for Python there are only a few. Here we develop a Python package called pyrichlet, for Bayesian nonparametric density estimation and clustering using various state-of-the-art Gaussian mixture models that generalize the well established Dirichlet process mixture, many of which are fairly new. Implementation is performed using Markov chain Monte Carlo techniques as well as variational Bayes methods. This article contains a detailed description of pyrichlet and examples for its usage with a real dataset.
Keywords:
density estimation
clustering
random partitions
Gaussian mixture
Dirichlet cess
geometric process
Pitman-Yor
Python
Journal
IF:
8.1
Papers:
616
Citations:
4.6W
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