| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 7547737 | Statistical Methodology | 2014 | 17 Pages |
Abstract
TMCMC is compared with MH using the well-known Challenger data, demonstrating the effectiveness of the former in the case of highly correlated variables. Moreover, we apply our methodology to a challenging posterior simulation problem associated with the geostatistical model of Diggle et al. (1998) [7], updating 160 unknown parameters jointly, using a deterministic transformation of a one-dimensional random variable. Remarkable computational savings as well as good convergence properties and acceptance rates are the results.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Somak Dutta, Sourabh Bhattacharya,