自适应非参数化对参数模型的扰动：广义贝叶斯方法
Adaptive Nonparametric Perturbations of Parametric Models with Generalized Bayes
AI总结：
本文提出一种自适应非参数化修正方法，用于改进参数贝叶斯模型的鲁棒性和效率，通过广义贝叶斯方法避免非参数贝叶斯因子计算，应用于单细胞RNA测序数据中的基因表达因果效应估计。
摘要：
参数贝叶斯建模为机器学习提供了强大而灵活的工具。然而，即使模型细节详尽，也可能错误，这会使推断不可信。本文介绍了一种新的半参数化修正方法，用于参数贝叶斯模型，当推断目标是真实数据分布的功能时。我们的起点是完全贝叶斯建模方法，明确考虑参数模型可能错误的可能性。渐近分析显示，该方法在模型不准确时具有鲁棒性，并且在参数模型接近真实情况时具有高效性。然而，完全贝叶斯方法在实际应用中的实用性受到进行推断和计算非参数贝叶斯因子的挑战。因此，我们提出了一种基于广义贝叶斯的新模型修正方法，完全避免计算非参数贝叶斯因子，但保留完全贝叶斯方法的鲁棒性和效率。我们通过估计单细胞RNA测序数据中的基因表达因果效应来展示我们的方法。总体而言，我们提供了一种新的高效方法，用于参数模型的鲁棒贝叶斯推断。
英文摘要：
Parametric Bayesian modeling offers a powerful and flexible toolbox for machine learning. Yet the model, however detailed, may still be wrong, and this can make inferences untrustworthy. In this paper we introduce a new class of semiparametric corrections for parametric Bayesian models, when the target of inference is a functional of the true data distribution. Our starting point is a fully Bayesian modeling approach, which explicitly accounts for the possibility that the parametric model is wrong. Asymptotic analysis shows that this approach is both robust to model misspecification and data efficient, achieving fast convergence when the parametric model is close to true. However, the fully Bayesian approach is limited in its practical usefulness by the challenges of conducting inference and computing a Bayes factor for a nonparametric model. We therefore propose a novel model correction based on generalized Bayes, which entirely avoids the need to compute a nonparametric Bayes factor, but preserves the robustness and efficiency of the fully Bayesian approach. We demonstrate our method by estimating causal effects of gene expression from single cell RNA sequencing data. Overall, we offer a new efficient approach to robust Bayesian inference with parametric models.
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