论文标题
贝叶斯模型比较大数据
Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data
论文作者
论文摘要
最近，已经提出了基于子采样和后近近似值的新方法，以将剩余的交叉验证（LOO）缩放到大型数据集中。尽管这些方法可以很好地估计单个模型的预测性能，但它们在模型比较中的功能较差。我们提出了一种有效的方法，用于通过将快速近似loo替代物与使用差估计量的差异估计器和供应证明有关缩放特征的供应证明来估算预测性能的差异。最终的方法可能是比以前的方法更有效的数量级，并且更适合模型比较。
Recently, new methods for model assessment, based on subsampling and posterior approximations, have been proposed for scaling leave-one-out cross-validation (LOO) to large datasets. Although these methods work well for estimating predictive performance for individual models, they are less powerful in model comparison. We propose an efficient method for estimating differences in predictive performance by combining fast approximate LOO surrogates with exact LOO subsampling using the difference estimator and supply proofs with regards to scaling characteristics. The resulting approach can be orders of magnitude more efficient than previous approaches, as well as being better suited to model comparison.
