PosCoSeA：基于后验协方差表示的贝叶斯模型计算高效的敏感性分析
{poscosea} : A Computationally Efficient Sensitivity Analysis for Bayesian Models using the posterior covariance representation
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中文总结 AI 辅助
本文针对贝叶斯模型敏感性分析的高计算成本问题，提出无需重复重拟合模型的PosCoSeA方法，经模拟与真实生态数据验证，还配套提供R包方便应用。
中文摘要 AI 辅助
贝叶斯方法是现代生态学与进化生物学数据分析的核心工具，它为建模复杂的数据生成过程提供了灵活框架，同时基于概率的经典主观解释量化不确定性。但贝叶斯推断可能给出误导性的不确定性度量，尤其当拟合模型无法充分表征真实数据生成过程时。尽管留k-折诊断法、自助重抽样等非参数方法在模型误设下提供更稳健的替代方案，但其计算成本过高，因需反复重拟合同一贝叶斯模型。本文提出后验协方差敏感性分析（PosCoSeA），一种无需重复重拟合模型即可近似留k-折诊断与自助重抽样的计算高效策略。通过模拟研究及真实生态数据应用评估方法性能，还提供实现这些方法的R包以方便实际应用。
英文摘要
Bayesian methods are essential in modern data analysis in ecology and evolutionary biology. They provide a flexible framework for modeling complex data-generating processes, while quantifying uncertainty based on the classical subjective interpretation of probability. However, Bayesian inference may provide misleading measures of uncertainty, particularly when the fitted model fails to adequately represent the true data-generating process. Although nonparametric approaches such as leave-k-out diagnostics and bootstrap resampling offer more robust alternatives under model misspecification, their computational cost is often too demanding because they require repeatedly refitting the same Bayesian model. In this paper, we introduce posterior covariance sensitivity analysis (PosCoSeA), a computationally efficient strategy for approximating leave-k-out diagnostics and bootstrap resampling without repeated model refitting. The performance of the methods is evaluated through both simulation studies and an application to real ecological data. We also provide an \textsf{R} package that implements these methods to facilitate their practical application.
