论文标题
强大的贝叶斯推断，以分散距离的离散结果
Robust Bayesian Inference for Discrete Outcomes with the Total Variation Distance
论文作者
论文摘要
如果数据表现出零通气，过度分散或污染，则很容易将离散结果的结果模型误认为。没有有关此错误指定的存在和性质的其他知识，模型推断和预测会受到不利影响。在这里，我们使用总变化距离（TVD）介绍了一种强大的基于差异的贝叶斯方法。在此过程中，我们解决并解决两个挑战：首先，我们研究了参数模型和数据生成机制之间TVD的计算有效估计器的收敛性和鲁棒性特性。其次，我们提供了一个有效的推理方法，该方法改编自Lyddon等人。 （2019年），对应于直接在数据生成机制上制定非信息性非参数。最后，我们从经验上证明，我们的方法是强大的，并且可以显着提高一系列模拟和现实世界数据的预测性能。
Models of discrete-valued outcomes are easily misspecified if the data exhibit zero-inflation, overdispersion or contamination. Without additional knowledge about the existence and nature of this misspecification, model inference and prediction are adversely affected. Here, we introduce a robust discrepancy-based Bayesian approach using the Total Variation Distance (TVD). In the process, we address and resolve two challenges: First, we study convergence and robustness properties of a computationally efficient estimator for the TVD between a parametric model and the data-generating mechanism. Second, we provide an efficient inference method adapted from Lyddon et al. (2019) which corresponds to formulating an uninformative nonparametric prior directly over the data-generating mechanism. Lastly, we empirically demonstrate that our approach is robust and significantly improves predictive performance on a range of simulated and real world data.
