截尾多正态分布的完全随机抽样
Perfectly random sampling of truncated multinormal distributions
摘要：
目标测量$μ$是随机向量在盒子$\cB$中的分布，盒子是有界区间的笛卡儿积。Gibbs采样器是具有不变测度$μ$的马尔可夫链。Gibbs采样器的“来自过去的耦合”构造用于显示动力学的遍历性并完美地模拟$μ$。然后实现了一种对具有截断为$\cB$的多正态分布的向量进行采样的算法。
英文摘要：
The target measure $μ$ is the distribution of a random vector in a box $\cB$, a Cartesian product of bounded intervals. The Gibbs sampler is a Markov chain with invariant measure $μ$. A ``coupling from the past'' construction of the Gibbs sampler is used to show ergodicity of the dynamics and to perfectly simulate $μ$. An algorithm to sample vectors with multinormal distribution truncated to $\cB$ is then implemented.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
