THE WANG-LANDAU ALGORITHM IN GENERAL STATE SPACES: APPLICATIONS AND CONVERGENCE ANALYSIS

被引：0

作者：

Atchade, Yves F. ^{[1
]}

Liu, Jun S. ^{[2
]}

机构：

[1] Univ Michigan, Dept Stat, Ann Arbor, MI 48109 USA

[2] Harvard Univ, Dept Stat, Cambridge, MA 02138 USA

来源：

STATISTICA SINICA | 2010年 / 20卷 / 01期

基金：

美国国家科学基金会; 加拿大自然科学与工程研究理事会;

关键词：

Adaptive MCMC; geometric ergodicity; Monte Carlo methods; multicanonical sampling; stochastic approximation; trans-dimensional MCMC; Wang-Landau algorithm; CHAIN MONTE-CARLO; STOCHASTIC-APPROXIMATION;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

The Wang-Landau algorithm (Wang and Landau (2001)) is a recent Monte Carlo method that has generated much interest in the Physics literature clue to some spectacular simulation performances The objective of this paper is two-fold First, we show that the algorithm can be naturally extended to more general state spaces and used to improve on Markov Chain Monte Cat-to schemes of more interest in Statistics In a second part, we study asymptotic behaviors of the algorithm We show that with an appropriate choice of the step-size, the algorithm is consistent and a strong law of large numbers holds under some fairly mild conditions We have also shown by simulations the potential advantage of the WL, algorithm for problems in Bayesian inference

引用

页码：209 / 233

页数：25

共 21 条

[1] Stability of stochastic approximation under verifiable conditions [J].