Bayesian model averaging in model-based clustering and density estimation

Niamh Russell; Thomas Brendan Murphy; Adrian E Raftery

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arXiv · 1506.09035

Bayesian model averaging in model-based clustering and density estimation

Niamh Russell·Thomas Brendan Murphy·Adrian E Raftery

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Abstract

We propose Bayesian model averaging (BMA) as a method for postprocessing the results of model-based clustering. Given a number of competing models, appropriate model summaries are averaged, using the posterior model probabilities, instead of being taken from a single "best" model. We demonstrate the use of BMA in model-based clustering for a number of datasets. We show that BMA provides a useful summary of the clustering of observations while taking model uncertainty into account. Further, we show that BMA in conjunction with model-based clustering gives a competitive method for density estimation in a multivariate setting. Applying BMA in the model-based context is fast and can give enhanced modeling performance.

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BibTeX RIS

Niamh Russell, Thomas Brendan Murphy, Adrian E Raftery. 2015-06-30. Bayesian model averaging in model-based clustering and density estimation. https://arxiv.org/abs/1506.09035

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