Adaptive Multiple Importance Sampling

Details Adaptive Multiple Importance Sampling Adaptive Multiple Importance Sampling

2009-07-07

arxiv.org/abs/0907.1254v5

Statistics

Computation

20 pages, 3 figures, revised version

Scientific paper

The Adaptive Multiple Importance Sampling (AMIS) algorithm is aimed at an optimal recycling of past simulations in an iterated importance sampling scheme. The difference with earlier adaptive importance sampling implementations like Population Monte Carlo is that the importance weights of all simulated values, past as well as present, are recomputed at each iteration, following the technique of the deterministic multiple mixture estimator of Owen and Zhou (2000). Although the convergence properties of the algorithm cannot be fully investigated, we demonstrate through a challenging banana shape target distribution and a population genetics example that the improvement brought by this technique is substantial.

Affiliated with

Also associated with

No associations

Rating

Adaptive Multiple Importance Sampling does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Adaptive Multiple Importance Sampling, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Adaptive Multiple Importance Sampling will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-356439