On a Loss-based prior for the number of components in mixture models
Methodology
2018-09-05 v2
Abstract
We propose a prior distribution for the number of components of a finite mixture model. The novelty is that the prior distribution is obtained by considering the loss one would incur if the true value representing the number of components were not considered. The prior has an elegant and easy to implement structure, which allows to naturally include any prior information one may have as well as to opt for a default solution in cases where this information is not available. The performance of the prior, and comparison with existing alternatives, is studied through the analysis of both real and simulated data.
Keywords
Cite
@article{arxiv.1807.07874,
title = {On a Loss-based prior for the number of components in mixture models},
author = {Clara Grazian and Cristiano Villa and Brunero Liseo},
journal= {arXiv preprint arXiv:1807.07874},
year = {2018}
}