Efficient implementation of the Metropolis-Hastings algorithm, with application to the Cormack-Jolly-Seber model
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Abstract
Judicious choice of candidate generating distributions improves efficiency of the Metropolis-Hastings algorithm. In Bayesian applications, it is sometimes possible to identify an approximation to the target posterior distribution; this approximate posterior distribution is a good choice for candidate generation. These observations are applied to analysis of the Cormack-Jolly-Seber model and its extensions. ?? Springer Science+Business Media, LLC 2007.
| Publication type | Conference Paper |
|---|---|
| Publication Subtype | Conference Paper |
| Title | Efficient implementation of the Metropolis-Hastings algorithm, with application to the Cormack-Jolly-Seber model |
| DOI | 10.1007/s10651-007-0037-9 |
| Volume | 15 |
| Issue | 1 |
| Year Published | 2008 |
| Language | English |
| Larger Work Title | Environmental and Ecological Statistics |
| First page | 79 |
| Last page | 87 |
| Google Analytic Metrics | Metrics page |