Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership
Gregor Zens ()
Papers from arXiv.org
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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Date: 2018-09, Revised 2019-01
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