摘要
证明利用 Gibbs抽样法从后验分布 p(· |y)产生的 Markov链转移核有不变概率测度 p(· |y) .利用Gibbs抽样法、Monte Carlo积分和条件概率公式相结合的方法对多元分布的边际密度进行了估计 ,并且证明了在一定条件下所得的边际密度估计几乎处处收敛到真实的边际密度 .
A proof was given that a Markov transition kernal from posterior distribution p(·|y) by Gibbs sampling has an invariant distribution p(·|y) . To estimate the marginal densities of multidimensional distribution, Gibbs sampler in conjunction with Monte Carlo inte gration and conditional probability formula was used. Under some conditions, the estimated marginal densities almost converge to the true marginal densities everywhere. Two examples were given to illustrate this result.
出处
《北京理工大学学报》
EI
CAS
CSCD
北大核心
2002年第1期16-19,共4页
Transactions of Beijing Institute of Technology
