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

• Tác giả: Gregor Zens
• Ngôn ngữ: eng
• Ký hiệu phân loại: 003.1 System identification
• Thông tin xuất bản: 2018
• Bộ sưu tập: Metadata
• ID: 162219

Tóm tắt

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.