论文标题
$γ$ -ABC：基于强大的散度估计器的异常值近似贝叶斯计算
$γ$-ABC: Outlier-Robust Approximate Bayesian Computation Based on a Robust Divergence Estimator
论文作者
论文摘要
近似贝叶斯计算（ABC）是在各种应用中采用的无似然推理方法。但是，如果不适当地选择了数据差异度量，ABC可能对离群值敏感。在本文中，我们建议将最近的基于邻居的$γ$ divergence估算器作为数据差异度量。我们表明，我们的估计器具有称为重新延期属性的合适理论鲁棒性。此外，我们的估计器具有各种理想的特性，例如高灵活性，渐近无偏见，几乎确定的收敛性和线性时间计算复杂性。通过实验，我们证明我们的方法比现有的差异措施实现了明显更高的鲁棒性。
Approximate Bayesian computation (ABC) is a likelihood-free inference method that has been employed in various applications. However, ABC can be sensitive to outliers if a data discrepancy measure is chosen inappropriately. In this paper, we propose to use a nearest-neighbor-based $γ$-divergence estimator as a data discrepancy measure. We show that our estimator possesses a suitable theoretical robustness property called the redescending property. In addition, our estimator enjoys various desirable properties such as high flexibility, asymptotic unbiasedness, almost sure convergence, and linear-time computational complexity. Through experiments, we demonstrate that our method achieves significantly higher robustness than existing discrepancy measures.
