A Bayesian hierarchical model for related densities by using Pólya trees

Published

Journal Article

© 2019 Royal Statistical Society Bayesian hierarchical models are used to share information between related samples and to obtain more accurate estimates of sample level parameters, common structure and variation between samples. When the parameter of interest is the distribution or density of a continuous variable, a hierarchical model for continuous distributions is required. Various such models have been described in the literature using extensions of the Dirichlet process and related processes, typically as a distribution on the parameters of a mixing kernel. We propose a new hierarchical model based on the Pólya tree, which enables direct modelling of densities and enjoys some computational advantages over the Dirichlet process. The Pólya tree also enables more flexible modelling of the variation between samples, providing more informed shrinkage and permitting posterior inference on the dispersion function, which quantifies the variation between sample densities. We also show how the model can be extended to cluster samples in situations where the observed samples are believed to have been drawn from several latent populations.

Full Text

Duke Authors

Cited Authors

  • Christensen, J; Ma, L

Published Date

  • February 1, 2020

Published In

Volume / Issue

  • 82 / 1

Start / End Page

  • 127 - 153

Electronic International Standard Serial Number (EISSN)

  • 1467-9868

International Standard Serial Number (ISSN)

  • 1369-7412

Digital Object Identifier (DOI)

  • 10.1111/rssb.12346

Citation Source

  • Scopus