We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost. This is the first result which allows for asymptotically valid inference for entropic optimal transport between measures which are not necessarily discrete. In the compactly supported case, we complement these results with new, faster, convergence rates for the expected entropic transportation cost between empirical measures. Our proof is based on strengthening convergence results for dual solutions to the entropic optimal transport problem.

翻译：事实证明,我们对于以人口成本为核心的亚加苏西地区概率计量方法之间的载人运输成本有一个核心限制。 这是允许在不一定离散的计量方法之间对载人最佳运输进行不时有效的推断的第一个结果。 在得到紧密支持的案例中,我们用新的、更快的、综合的实验计量方法之间预期载人运输成本的速率来补充这些结果。 我们的证明是基于加强趋同结果,从而对载人最佳运输问题采取双重解决办法。