Latent factor models for density estimation

Published

Journal Article

Although discrete mixture modelling has formed the backbone of the literature on Bayesian density estimation, there are some well-known disadvantages. As an alternative to discrete mixtures, we propose a class of priors based on random nonlinear functions of a uniform latent variable with an additive residual. The induced prior for the density is shown to have desirable properties, including ease of centring on an initial guess, large support, posterior consistency and straightforward computation via Gibbs sampling. Some advantages over discrete mixtures, such as Dirichlet process mixtures of Gaussian kernels, are discussed and illustrated via simulations and an application. © 2014 Biometrika Trust.

Full Text

Duke Authors

Cited Authors

  • Kundu, S; Dunson, DB

Published Date

  • January 1, 2014

Published In

Volume / Issue

  • 101 / 3

Start / End Page

  • 641 - 654

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asu019

Citation Source

  • Scopus