We present the Local Self-Balancing sampler (LSB), a local Markov Chain Monte Carlo (MCMC) method for sampling in purely discrete domains, which is able to autonomously adapt to the target distribution and to reduce the number of target evaluations required to converge. LSB is based on (i) a parametrization of locally balanced proposals, (ii) a newly proposed objective function based on mutual information and (iii) a self-balancing learning procedure, which minimises the proposed objective to update the proposal parameters. Experiments on energy-based models and Markov networks show that LSB converges using a smaller number of queries to the oracle distribution compared to recent local MCMC samplers.

翻译：我们提出了当地自我平衡采样器(LSB),这是一种本地的Markov链条蒙特卡洛(MCMC)方法,用于纯粹独立域的采样,能够自主地适应目标分布,并减少为趋同所需的目标评价数量;LSB的依据是(一) 当地均衡提案的平衡化,(二) 以相互信息为基础的新提议的目标功能,(三) 自我平衡学习程序,最大限度地减少更新提议参数的拟议目标;关于能源模型和Markov网络的实验显示,与最近的MCMC采样器相比,基于能源模型和Markov网络的实验显示,使用较少数量的查询,汇集了对甲骨文分布的查询。