The effects of three different priors for variance parameters in the normal-mean hierarchical model
type: CreativeWork
description: Many prior distributions are suggested for variance parameters in the hierarchical model. The “Non-informative” interval of the conjugate inverse-gamma prior might cause problems. I consider three priors – conjugate inverse-gamma, log-normal and truncated normal for the variance parameters and do the numerical analysis on Gelman’s 8-schools data. Then with the posterior draws, I compare the Bayesian credible intervals of parameters using the three priors. I use predictive distributions to do predictions and then discuss the differences of the three priors suggested.
abstract: Many prior distributions are suggested for variance parameters in the hierarchical model. The “Non-informative” interval of the conjugate inverse-gamma prior might cause problems. I consider three priors – conjugate inverse-gamma, log-normal and truncated normal for the variance parameters and do the numerical analysis on Gelman’s 8-schools data. Then with the posterior draws, I compare the Bayesian credible intervals of parameters using the three priors. I use predictive distributions to do predictions and then discuss the differences of the three priors suggested.
author: Chen, Zhu, 1985-
language: eng
keywords: Bayesian inference, Conjugate prior, Log-normal, Truncated normal, Hierarchical model, Metropolis-Hastings within Gibbs, Gibbs sampler
date published: 2010-05
date modified: 2026-07-24T05:01:24Z
provider: University of Texas
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Texas at Austin

PropertyValue
property ID: Handle
value: http://hdl.handle.net/2152/ETD-UT-2010-05-1516