| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 415393 | Computational Statistics & Data Analysis | 2014 | 12 Pages |
Abstract
A modified conditional Metropolis–Hastings sampler for general state spaces is introduced. Under specified conditions, this modification can lead to substantial gains in statistical efficiency while maintaining the overall quality of convergence. Results are illustrated in two settings: a toy bivariate Normal model and a Bayesian version of the random effects model.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Alicia A. Johnson, James M. Flegal,