Name
Abstract | ||
|---|---|---|
In this paper, we first propose a noniterative sampling method to obtain an i.i.d. sample approximately from posteriors by combining the inverse Bayes formula, sampling/importance resampling and posterior mode estimates. We then propose a new exact algorithm to compute posteriors by improving the PMDA-Exact using the sampling-wise IBF. If the posterior mode is available from the EM algorithm, then these two algorithms compute posteriors well and eliminate the convergence problem of Markov Chain Monte Carlo methods. We show good performances of our methods by some examples. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s00180-007-0085-5 | Computational Statistics |
Keywords | DocType | Volume |
importance resampling,Markov Chain Monte Carlo,inverse Bayes formula,noniterative algorithm,good performance,posterior mode,noniterative sampling method,new exact algorithm,posterior mode estimate,EM algorithm,convergence problem,bayesian computation · data augmentation · em algorithm · inverse bayes formula · sampling/importance resampling · pmda-exact | Journal | 23 |
Issue | ISSN | Citations |
3 | 1613-9658 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Yang | 1 | 7 | 1.52 |
| Guohua Zou | 2 | 12 | 5.72 |
| Yu Zhao | 3 | 0 | 0.34 |