论文标题
具有软约束的蒙特卡洛：表面增强采样器
Monte Carlo with Soft Constraints: the Surface Augmented Sampler
论文作者
论文摘要
我们描述了一种MCMC方法，用于采样具有软约束的分布，这几乎是但不完全满足的约束。我们采样了总分布，该分布是目标软分布与附近的硬分布在约束表面上支持的凸组合。硬分布移动导致柔软参数中均匀的性能。与Holmes-Cerfon分层采样器相关的开关移动可实现目标软分布。计算实验验证了在软限制限制下的性能是统一的。
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.
