Skip to main content

On a loss-based prior for the number of components in mixture models

Grazian, Clara, Villa, Cristiano, Liseo, Brunero (2020) On a loss-based prior for the number of components in mixture models. Statistics and Probability Letters, 158 . Article Number 108656. ISSN 0167-7152. (doi:10.1016/j.spl.2019.108656) (KAR id:77890)

PDF Author's Accepted Manuscript
Language: English


Download (735kB) Preview
[thumbnail of GVL_SPL_3.pdf]
Preview
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL:
https://doi.org/10.1016/j.spl.2019.108656

Abstract

We introduce a prior distribution for the number of components of a mixture model. The prior considers the worth of each possible mixture, measured by a loss function with two components: one measures the loss in information in choosing the wrong mixture and one the loss due to complexity.

Item Type: Article
DOI/Identification number: 10.1016/j.spl.2019.108656
Uncontrolled keywords: Mixture models, Bayesian inference, Default priors, Loss-based priors, Clustering
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: Cristiano Villa
Date Deposited: 26 Oct 2019 13:24 UTC
Last Modified: 09 Dec 2022 04:35 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/77890 (The current URI for this page, for reference purposes)
Villa, Cristiano: https://orcid.org/0000-0002-2670-2954
  • Depositors only (login required):

Downloads

Downloads per month over past year