Adaptive Importance Sampling in General Mixture Classes

Statistics – Computation

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Removed misleading comment in Section 2

Scientific paper

10.1007/s11222-008-9059-x

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 importance sampling performances, as measured by an entropy criterion. The method is shown to be applicable to a wide class of importance sampling densities, which includes in particular mixtures of multivariate Student t distributions. The performances of the proposed scheme are 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.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Adaptive Importance Sampling in General Mixture Classes 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 Importance Sampling in General Mixture Classes, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Adaptive Importance Sampling in General Mixture Classes will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-456071

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.