标题： 自适应高斯copula ABC 显示英文标题

标题： Adaptive Gaussian Copula ABC

摘要： 近似贝叶斯计算（ABC）是一组当似然函数不可解析但可以从模型中抽样时用于贝叶斯推断的技术。 本文提出了一种基于两种经典ABC方法——回归ABC和顺序ABC的组合的简单而有效的ABC算法。 关键思想是，我们不是直接学习后验分布，而是首先针对另一个可以通过现有方法准确学习的辅助分布，通过该辅助分布，然后借助高斯copula逐步学习所需的后验分布。 在此过程中，模型的复杂性会根据手头的数据自适应地改变。 在合成数据集以及三个现实世界的推断任务上的实验表明，所提出的方法快速、准确且易于使用。 显示英文摘要

摘要： Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents a simple yet effective ABC algorithm based on the combination of two classical ABC approaches --- regression ABC and sequential ABC. The key idea is that rather than learning the posterior directly, we first target another auxiliary distribution that can be learned accurately by existing methods, through which we then subsequently learn the desired posterior with the help of a Gaussian copula. During this process, the complexity of the model changes adaptively according to the data at hand. Experiments on a synthetic dataset as well as three real-world inference tasks demonstrates that the proposed method is fast, accurate, and easy to use.