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

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  • Gregor Zens

    (Vienna University of Economics and Business)

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.

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  • Gregor Zens, 2019. "Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1019-1051, December.
    • Handle: RePEc:spr:advdac:v:13:y:2019:i:4:d:10.1007_s11634-019-00353-y
    DOI: 10.1007/s11634-019-00353-y
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