Bayesian density estimation for compositional data using random Bernstein polynomials


Journal Article

© 2015 Elsevier B.V. We propose a Bayesian nonparametric procedure for density estimation for data in a d-dimensional simplex. To this aim, we propose a prior distribution on probability measures based on a modified class of multivariate Bernstein polynomials. The model for the probability distribution corresponds to a mixture of Dirichlet distributions, with random weights and a random number of components. Theoretical properties of the proposal are provided, including posterior consistency and concentration rates of the posterior distribution.

Full Text

Cited Authors

  • Barrientos, AF; Jara, A; Quintana, FA

Published Date

  • January 1, 2015

Published In

Volume / Issue

  • 166 /

Start / End Page

  • 116 - 125

International Standard Serial Number (ISSN)

  • 0378-3758

Digital Object Identifier (DOI)

  • 10.1016/j.jspi.2015.01.006

Citation Source

  • Scopus