@article{Elvira2015bb,

title = {Efficient Multiple Importance Sampling Estimators},

author = {Victor Elvira and Luca Martino and David Luengo and Monica F Bugallo},

url = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7105865},

doi = {10.1109/LSP.2015.2432078},

issn = {1070-9908},

year  = {2015},

date = {2015-10-01},

journal = {IEEE Signal Processing Letters},

volume = {22},

number = {10},

pages = {1757--1761},

publisher = {IEEE},

abstract = {Multiple importance sampling (MIS) methods use a set of proposal distributions from which samples are drawn. Each sample is then assigned an importance weight that can be obtained according to different strategies. This work is motivated by the trade-off between variance reduction and computational complexity of the different approaches (classical vs. deterministic mixture) available for the weight calculation. A new method that achieves an efficient compromise between both factors is introduced in this letter. It is based on forming a partition of the set of proposal distributions and computing the weights accordingly. Computer simulations show the excellent performance of the associated partial deterministic mixture MIS estimator.},

keywords = {Adaptive importance sampling, classical mixture approach, computational complexity, Computational efficiency, Computer Simulation, deterministic mixture, estimation theory, Journal, Monte Carlo methods, multiple importance sampling, multiple importance sampling estimator, partial deterministic mixture MIS estimator, Proposals, signal sampling, Sociology, Standards, variance reduction, weight calculation},

pubstate = {published},

tppubtype = {article}

}