Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
本文提供了分析离散时间、一般状态空间马尔可夫链收敛性的通用方法，可用于吉布斯采样等随机模拟算法，给出模拟运行时间的严格先验界，并应用于双变量正态和分层泊松模型。
Abstract 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 be 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.