Latent Factor Models for Density Estimation

Details Latent Factor Models for Density Estimation Latent Factor Models for Density Estimation

2011-08-12

arxiv.org/abs/1108.2720v2

Mathematics

Statistics Theory

Scientific paper

Although discrete mixture modeling has formed the backbone of the literature on Bayesian density estimation, there are some well known disadvantages. We propose an alternative 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 centering on an initial guess for the density, 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 epidemiology application.

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