We describe an MCMC method for sampling distributions with soft constraints, which are constraints that are almost but not exactly satisfied. We sample a total distribution that is a convex combination of the target soft distribution with the nearby hard distribution supported on the constraint surface. Hard distribution moves lead to performance that is uniform in the softness parameter. On and Off moves related to the Holmes-Cerfon Stratification Sampler enable sampling the target soft distribution. Computational experiments verify that performance is uniform in the soft constraints limit.

翻译：我们描述的是微软限制的采样分布的MCMC方法,这些是几乎但并不完全满足的限制因素。我们抽样的分布总量是目标软分布与附近限制表面所支持的硬分布的分母组合。硬分布移动导致软性参数的性能一致。与福尔摩斯-Cerfon采样器有关的上下移动使得能够对目标软分布进行取样。计算实验证实软约束限度的性能一致。