Latent factor models for density estimation
Journal Article (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