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Posterior impropriety of some sparse Bayesian learning models

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  • Dixit, Anand
  • Roy, Vivekananda

Abstract

Sparse Bayesian learning models are typically used for prediction in datasets with significantly greater number of covariates than observations. Such models often take a reproducing kernel Hilbert space (RKHS) approach to carry out the task of prediction and can be implemented using either proper or improper priors. In this article we show that a few sparse Bayesian learning models in the literature, when implemented using improper priors, lead to improper posteriors.

Suggested Citation

  • Dixit, Anand & Roy, Vivekananda, 2021. "Posterior impropriety of some sparse Bayesian learning models," Statistics & Probability Letters, Elsevier, vol. 171(C).
    • Handle: RePEc:eee:stapro:v:171:y:2021:i:c:s0167715221000018
    DOI: 10.1016/j.spl.2021.109039
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    References listed on IDEAS

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    1. Bani K. Mallick & Debashis Ghosh & Malay Ghosh, 2005. "Bayesian classification of tumours by using gene expression data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 219-234, April.
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