In this section
@inproceedings{Wang:2016:10.1109/ITW.2016.7606863,
author = {Wang, Z and Ling, C},
doi = {10.1109/ITW.2016.7606863},
publisher = {IEEE},
title = {Symmetric mettropolis-within-Gibbs algorithm for lattice Gaussian sampling},
url = {http://dx.doi.org/10.1109/ITW.2016.7606863},
year = {2016}
}
TY - CPAPER
AB - As a key sampling scheme in Markov chain MonteCarlo (MCMC) methods, Gibbs sampling is widely used invarious research fields due to its elegant univariate conditionalsampling, especially in tacking with multidimensional samplingsystems. In this paper, a Gibbs-based sampler named as symmet-ric Metropolis-within-Gibbs (SMWG) algorithm is proposed forlattice Gaussian sampling. By adopting a symmetric Metropolis-Hastings (MH) step into the Gibbs update, we show the Markovchain arising from it is geometrically ergodic, which convergesexponentially fast to the stationary distribution. Moreover, byoptimizing its symmetric proposal distribution, the convergenceefficiency can be further enhanced.
AU - Wang,Z
AU - Ling,C
DO - 10.1109/ITW.2016.7606863
PB - IEEE
PY - 2016///
TI - Symmetric mettropolis-within-Gibbs algorithm for lattice Gaussian sampling
UR - http://dx.doi.org/10.1109/ITW.2016.7606863
ER -
