Symmetric mettropolis-within-Gibbs algorithm for lattice Gaussian sampling
File(s)bare_jrnl.pdf (235.01 KB)
Accepted version
Author(s)
Wang, Z
Ling, C
Type
Conference Paper
Abstract
As a key sampling scheme in Markov chain Monte
Carlo (MCMC) methods, Gibbs sampling is widely used in
various research fields due to its elegant univariate conditional
sampling, especially in tacking with multidimensional sampling
systems. In this paper, a Gibbs-based sampler named as symmet-
ric Metropolis-within-Gibbs (SMWG) algorithm is proposed for
lattice Gaussian sampling. By adopting a symmetric Metropolis-
Hastings (MH) step into the Gibbs update, we show the Markov
chain arising from it is geometrically ergodic, which converges
exponentially fast to the stationary distribution. Moreover, by
optimizing its symmetric proposal distribution, the convergence
efficiency can be further enhanced.
Carlo (MCMC) methods, Gibbs sampling is widely used in
various research fields due to its elegant univariate conditional
sampling, especially in tacking with multidimensional sampling
systems. In this paper, a Gibbs-based sampler named as symmet-
ric Metropolis-within-Gibbs (SMWG) algorithm is proposed for
lattice Gaussian sampling. By adopting a symmetric Metropolis-
Hastings (MH) step into the Gibbs update, we show the Markov
chain arising from it is geometrically ergodic, which converges
exponentially fast to the stationary distribution. Moreover, by
optimizing its symmetric proposal distribution, the convergence
efficiency can be further enhanced.
Date Issued
2016-10-27
Date Acceptance
2016-06-12
Citation
2016 IEEE Information Theory Workshop (ITW), 2016
Publisher
IEEE
Journal / Book Title
2016 IEEE Information Theory Workshop (ITW)
Copyright Statement
© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
Commission of the European Communities
Grant Number
317562
Source
IEEE Information theory workshop
Subjects
Science & Technology
Technology
Computer Science, Theory & Methods
Engineering, Electrical & Electronic
Computer Science
Engineering
Lattice Gaussian sampling
MCMC methods
Gibbs sampling
Metropolis-Hastings algorithm
Publication Status
Published
Start Date
2016-09-11
Finish Date
2016-09-14
Coverage Spatial
Cambridge, UK