Adaptive importance sampling in general mixture classes

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 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.

• Type: Journal
• Year: 2008
• Where: SAC
• Authors: Olivier Cappé, Randal Douc, Arnaud Guillin, Jean-Michel Marin, Christian P. Robert
• Keywords: Applied Computing, Component Parameters, Importance Sampling Densities, Mixture Importance, SAC 2008