@inproceedings{1afa816e6f944b49b276e9e281136bac,
title = "On sample generation and weight calculation in multiple importance sampling",
abstract = "Importance sampling is a Monte Carlo technique that approximates moments of target densities by means of weighted samples. These samples are traditionally drawn from a single proposal density. In multiple importance sampling (MIS) a set of different proposal densities is available. In this paper, we propose a formal framework that allows different ways of drawing samples from a set of proposals and different proper weighting functions that can be applied. In particular, we describe three sampling methods and five generic weighting functions. As proper sampling/weighting combinations, six unique MIS schemes (three of them are novel) are discussed throughout the paper. All the methods are analyzed in terms of the variance of the associated estimators, establishing a ranking regarding their performance.",
keywords = "Bayesian inference, Monte Carlo methods, multiple importance sampling",
author = "Victor Elvira and Luca Martino and David Luengo and Bugallo, \{Monica F.\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 ; Conference date: 08-11-2015 Through 11-11-2015",
year = "2016",
month = feb,
day = "26",
doi = "10.1109/ACSSC.2015.7421441",
language = "English",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "1709--1713",
editor = "Matthews, \{Michael B.\}",
booktitle = "Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015",
}