论文标题
在具有可能性理论的复杂模型中，强大的贝叶斯推断
Robust Bayesian inference in complex models with possibility theory
论文作者
论文摘要
我们建议在可能存在异常值的复杂环境中鲁棒性贝叶斯推断问题的一般解决方案。实际上，在涉及大型和复杂数据集的许多应用中，强大的贝叶斯分析的自动化很重要。提出的解决方案依赖于基于可能性理论的贝叶斯推论的重新制定，并利用了这样的观察，即在这种情况下，数据的边际可能性评估了先前和可能性之间的一致性，而不是模型适应性。我们的方法不需要以其最简单的形式的其他参数，并且与非持续解决方案相比，对计算复杂性的影响有限。通过在模拟和真实数据的应用程序（包括矩阵估计和变更点检测）上，我们的解决方案的通用性证明了。
We propose a general solution to the problem of robust Bayesian inference in complex settings where outliers may be present. In practice, the automation of robust Bayesian analyses is important in the many applications involving large and complex datasets. The proposed solution relies on a reformulation of Bayesian inference based on possibility theory, and leverages the observation that, in this context, the marginal likelihood of the data assesses the consistency between prior and likelihood rather than model fitness. Our approach does not require additional parameters in its simplest form and has a limited impact on the computational complexity when compared to non-robust solutions. The generality of our solution is demonstrated via applications on simulated and real data including matrix estimation and change-point detection.
