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

Gregor Zens ()

Advances in Data Analysis and Classification, 2019, vol. 13, issue 4, No 9, 1019-1051

Abstract: 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.

Keywords: Mixture-of-experts; Classification; Shrinkage; Bayesian inference; Normal gamma prior; 62F15; 62J07; 62H30; 90-08 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s11634-019-00353-y

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