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A noniterative sampling method for computing posteriors in the structure of EM-type algorithms
| Content Provider | Semantic Scholar |
|---|---|
| Author | Tan, Ming Tian, Guo-Liang Ng, Kai Wang |
| Copyright Year | 2003 |
| Abstract | We propose a noniterative sampling approach by combining the inverse Bayes formulae (IBF), sampling/importance resampling and posterior mode esti- mates from the Expectation/Maximization (EM) algorithm to obtain an i.i.d. sam- ple approximately from the posterior distribution for problems where the EM-type algorithms apply. The IBF shows that the posterior is proportional to the ratio of two conditional distributions and its numerator provides a natural class of built-in importance sampling functions (ISFs) directly from the model specification. Given that the posterior mode by an EM-type algorithm is relatively easy to obtain, a best ISF can be identified by using that posterior mode, which results in a large overlap area under the target density and the ISF. We show why this procedure works the- oretically. Therefore, the proposed method provides a novel alternative to perfect sampling and eliminates the convergence problems of Markov chain Monte Carlo methods. We first illustrate the method with a proof-of-principle example and then apply the method to hierarchical (or mixed-effects) models for longitudinal data. We conclude with a discussion. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www3.stat.sinica.edu.tw/statistica/oldpdf/A13n34.pdf |
| Alternate Webpage(s) | http://hub.hku.hk/bitstream/10722/45365/1/95254.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
