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

Publication: Scientific journalJournal articlepeer-review


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.
Original languageEnglish
Pages (from-to)1019 - 1051
JournalAdvances in Data Analysis and Classification
Issue number4
Publication statusPublished - 2019

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