论文标题
强大的近似贝叶斯计算：调整方法
Robust Approximate Bayesian Computation: An Adjustment Approach
论文作者
论文摘要
我们提出了一种近似贝叶斯计算（ABC）的新方法，该方法试图满足假定模型的可能错误指定。这种新方法可以同样应用于基于排斥的ABC和流行的回归调整ABC。我们证明，这种新方法减轻了回归调整后的ABC的不良性能，而ABC可能会在模型被弄清楚时发生。此外，这种新的调整方法使我们能够检测到所观察到的数据的哪些特征不能由假定的模型可靠地重现。一系列模拟和经验的例子说明了这种新方法。
We propose a novel approach to approximate Bayesian computation (ABC) that seeks to cater for possible misspecification of the assumed model. This new approach can be equally applied to rejection-based ABC and to popular regression adjustment ABC. We demonstrate that this new approach mitigates the poor performance of regression adjusted ABC that can eventuate when the model is misspecified. In addition, this new adjustment approach allows us to detect which features of the observed data can not be reliably reproduced by the assumed model. A series of simulated and empirical examples illustrate this new approach.
