Differential evolution Markov chain with snooker updater and fewer chains [electronic resource]
Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by si...
Saved in:
| Online Access: |
Online Access |
|---|---|
| Corporate Author: | |
| Format: | Government Document Electronic eBook |
| Language: | English |
| Published: |
Washington, D.C. : Oak Ridge, Tenn. :
United States. Dept. of Energy ; distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy,
2008.
|
| Subjects: |
| Summary: | Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by simulation and real examples that DE-MC can work for d up to 50--100 with fewer parallel chains (e.g. N=3) by exploiting information from their past by generating jumps from differences of pairs of past states. This approach extends the practical applicability of DE-MC and is shown to be about 5--26 times more efficient than the optimal Normal random walk Metropolis sampler for the 97.5% point of a variable from a 25--50 dimensional Student T₃ distribution. In a nonlinear mixed effects model example the approach outperformed a block-updater geared to the specific features of the model. |
|---|---|
| Item Description: | Published through the Information Bridge: DOE Scientific and Technical Information. 01/01/2008. "la-ur-08-07125" " la-ur-08-7125" Statistics and Computing 18 4 ISSN 0960-3174 FT. Vrugt, Jasper A; Ter Braak, Cajo J F. |