论文标题
MCMC的“ Barker Dynamics”崭新
A fresh take on 'Barker dynamics' for MCMC
论文作者
论文摘要
我们研究了基于“ Barker Dynamics”的最近引入的基于梯度的Markov Chain Monte Carlo方法。我们从第一原理中提供了该方法的完整推导，将其放置在更广泛的连续时间马尔可夫跳跃过程中。然后，我们在具有不平衡数据的具有挑战性的不平衡的逻辑回归示例上以数值方式评估Barker方法，特别表明该算法在目标分布中对不规则性（在这种情况下是高度的偏差）非常健壮。
We study a recently introduced gradient-based Markov chain Monte Carlo method based on 'Barker dynamics'. We provide a full derivation of the method from first principles, placing it within a wider class of continuous-time Markov jump processes. We then evaluate the Barker approach numerically on a challenging ill-conditioned logistic regression example with imbalanced data, showing in particular that the algorithm is remarkably robust to irregularity (in this case a high degree of skew) in the target distribution.
