Name
Abstract | ||
|---|---|---|
In this paper, we propose an adaptive algorithm that iteratively updates both the weights and component parameters of a mixture importance sampling density so as to optimise the performance of importance sampling, as measured by an entropy criterion. The method, called M-PMC, is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performance of the proposed scheme is studied on both artificial and real examples, highlighting in particular the benefit of a novel Rao-Blackwellisation device which can be easily incorporated in the updating scheme. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s11222-008-9059-x | Statistics and Computing |
Keywords | Field | DocType |
Importance sampling,Adaptive Monte Carlo,Mixture model,Entropy,Kullback-Leibler divergence,EM algorithm,Population Monte Carlo | Slice sampling,Rejection sampling,Importance sampling,Umbrella sampling,Mathematical optimization,Expectation–maximization algorithm,Monte Carlo integration,Adaptive algorithm,Statistics,Mixture model,Mathematics | Journal |
Volume | Issue | ISSN |
18 | 4 | Statistics and Computing 18, 4 (2008) 447-459 |
Citations | PageRank | References |
57 | 5.14 | 1 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| O. Cappe | 1 | 2112 | 207.95 |
| Randal Douc | 2 | 58 | 6.21 |
| Arnaud Guillin | 3 | 57 | 5.48 |
| Jean-Michel Marin | 4 | 156 | 20.95 |
| Christian P. Robert | 5 | 199 | 28.86 |