Name
Abstract | ||
|---|---|---|
Simulated tempering is a popular method of allowing MCMC algorithms to move between modes of a multimodal target density \(\pi \). One problem with simulated tempering for multimodal targets is that the weights of the various modes change for different inverse-temperature values, sometimes dramatically so. In this paper, we provide a fix to overcome this problem, by adjusting the mode weights to be preserved (i.e. constant) over different inverse-temperature settings. We then apply simulated tempering algorithms to multimodal targets using our mode weight correction. We present simulations in which our weight-preserving algorithm mixes between modes much more successfully than traditional tempering algorithms. We also prove a diffusion limit for an version of our algorithm, which shows that under appropriate assumptions, our algorithm mixes in time \(O(d [\log d]^2)\). |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11222-019-09863-3 | Statistics and Computing |
Keywords | DocType | Volume |
Simulated tempering, Parallel tempering, MCMC, Multimodality and Monte Carlo | Journal | 30 |
Issue | ISSN | Citations |
1 | 1573-1375 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nicholas G. Tawn | 1 | 0 | 0.34 |
| Gareth Roberts | 2 | 368 | 42.35 |
| Jeffrey S. Rosenthal | 3 | 357 | 43.06 |