Name
Abstract | ||
|---|---|---|
Multiple proposal Markov chain Monte Carlo (MP-MCMC) as introduced by Calderhead (2014) allow for computationally efficient and parallelisable inference, whereby multiple states are proposed and computed simultaneously. In this paper, we improve the resulting integral estimators by sequentially using the multiple states within a RaoBlackwellised estimator. We further propose a novel adaptive Rao-Blackwellised MP-MCMC algorithm, which generalises the adaptive MCMC algorithm introduced by Haario et al. (2001) to allow for multiple proposals. We prove its asymptotic unbiasedness, and demonstrate significant improvements in sampling efficiency through numerical studies. |
Year | Venue | DocType |
|---|---|---|
2021 | 24TH INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND STATISTICS (AISTATS) | Conference |
Volume | ISSN | Citations |
130 | 2640-3498 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Schwedes | 1 | 0 | 0.34 |
| Ben Calderhead | 2 | 98 | 9.08 |