Adaptive Rejection Sampling for Gibbs Sampling
提出一种从单变量对数凹概率密度函数中进行拒绝采样的自适应方法，拒绝包络和挤压函数逐步收敛到密度函数，适用于密度计算昂贵的场景，如贝叶斯模型中的吉布斯采样。
We propose a method for rejection sampling from any univariate log‐concave probability density function. The method is adaptive: As sampling proceeds, the rejection envelope and the squeezing function converge to the density function. The rejection envelope and squeezing function are piece‐wise exponential functions, the rejection envelope touching the density at previously sampled points, and the squeezing function forming arcs between those points of contact. The technique is intended for situations where evaluation of the density is computationally expensive, in particular for applications of Gibbs sampling to Bayesian models with non‐conjugacy. We apply the technique to a Gibbs sampling analysis of monoclonal antibody reactivity.