MINORIZATION CONDITIONS AND CONVERGENCE-RATES FOR MARKOV-CHAIN MONTE-CARLO

被引:272
作者
ROSENTHAL, JS
机构
关键词
BIVARIATE NORMAL MODEL; COUPLING; DRIFT CONDITION; GIBBS SAMPLER; HARRIS RECURRENCE; HIERARCHICAL POISSON MODEL; METROPOLIS-HASTINGS ALGORITHM; REGENERATION TIME;
D O I
10.2307/2291067
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
General methods are provided for analyzing the convergence of discrete-time, general state-space Markov chains, such as those used in stochastic simulation algorithms including the Gibbs sampler. The methods provide rigorous, a priori bounds on how long these simulations should he run to give satisfactory results. Results are applied to two models of the Gibbs sampler: a bivariate normal model, and a hierarchical Poisson model (with gamma conditionals). The methods use the notion of minorization conditions for Markov chains.
引用
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页码:558 / 566
页数:9
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