Laplace Variational Approximation for Semiparametric Regression in the Presence of Heteroskedastic Errors.

ABSTRACT: Variational approximations provide fast, deterministic alternatives to Markov Chain Monte Carlo for Bayesian inference on the parameters of complex, hierarchical models. Variational approximations are often limited in practicality in the absence of conjugate posterior distributions. Recent work has focused on the application of variational methods to models with only partial conjugacy, such as in semiparametric regression with heteroskedastic errors. Here, both the mean and log variance functions are modeled as smooth functions of covariates. For this problem, we derive a mean field variational approximation with an embedded Laplace approximation to account for the non-conjugate structure. Empirical results with simulated and real data show that our approximate method has significant compu

SUBMITTER: Bugbee BD 

PROVIDER: S-EPMC5033129 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature