Mixture models with decreasing weights
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
Major/Subject
Mcode
Degree programme
Language
en
Pages
13
Series
Computational Statistics & Data Analysis, Volume 179
Abstract
Decreasing weight prior distributions for mixture models play an important role in nonparametric Bayesian inference. Various random probability measures with decreasing weights have been previously explored and it has been shown that they provide an efficient alternative to the more traditional Dirichlet process mixture model. This ordering of the weights implicitly alleviates the so-called label switching problem, as larger weights correspond to larger groups. A general procedure to define any decreasing weights model based on a characterization of a discrete random variable which also allows for an easy and generic sampling algorithm for estimating the model is provided. An exact representation for the number of expected components is given. Finally, the performance of the mixture model on simulated data sets is investigated numerically.Description
Funding Information: The authors are grateful for the comments and suggestions from two reviewers and an Associate Editor on an earlier version of the paper which have contributed to a substantial improvement. This article is dedicated to the memory of Spyridon J. Hatjispyros. A precious advisor, colleague and friend who is greatly missed. Publisher Copyright: © 2022 The Author(s)
Keywords
Other note
Citation
Hatjispyros, S J, Merkatas, C & Walker, S G 2023, 'Mixture models with decreasing weights', Computational Statistics & Data Analysis, vol. 179, 107651. https://doi.org/10.1016/j.csda.2022.107651