Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership

Gregor Zens

Publikation: Wissenschaftliche FachzeitschriftOriginalbeitrag in FachzeitschriftBegutachtung

37 Downloads (Pure)

Abstract

A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.
OriginalspracheEnglisch
Seiten (von - bis)1019 - 1051
FachzeitschriftAdvances in Data Analysis and Classification
Jahrgang13
Ausgabenummer4
DOIs
PublikationsstatusVeröffentlicht - 2019

Zitat