Assessment and Propagation of Model Uncertainty
模型不确定性的评估与传播AI 解读 ↓
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模型不确定性的评估与传播
- 研究问题
- 推断与预测若只考虑既定模型内的参数不确定性，而忽略模型结构选择的不确定性，会如何影响不确定性校准？
- 方法与思路
- 作者讨论通过贝叶斯方法评估并传播结构不确定性，并以石油价格预测和美国航天飞机灾难性失效概率估计为例。
- 材料陈述的贡献
- 摘要指出选定单一模型后视其为正确可能低估不确定性，并讨论计算进展使贝叶斯处理方法更易实际实施。
- 阅读关注点
- 应核对结构与参数不确定性的联合处理、模型集合及先验设定，以及两项应用中不确定性校准的评估方式。
- 阅读边界
- 仅据Crossref摘要；摘要未说明具体模型整合算法、先验敏感性或应用结果，不能断言方法普遍改善预测表现。
原文摘要
SUMMARY In most examples of inference and prediction, the expression of uncertainty about unknown quantities y on the basis of known quantities x is based on a model M that formalizes assumptions about how x and y are related. M will typically have two parts: structural assumptions S, such as the form of the link function and the choice of error distribution in a generalized linear model, and parameters θ whose meaning is specific to a given choice of S. It is common in statistical theory and practice to acknowledge parametric uncertainty about θ given a particular assumed structure S; it is less common to acknowledge structural uncertainty about S itself. A widely used approach involves enlisting the aid of x to specify a plausible single ‘best’ choice S* for S, and then proceeding as if S* were known to be correct. In general this approach fails to assess and propagate structural uncertainty fully and may lead to miscalibrated uncertainty assessments about y given x. When miscalibration occurs it will often result in understatement of inferential or predictive uncertainty about y, leading to inaccurate scientific summaries and overconfident decisions that do not incorporate sufficient hedging against uncertainty. In this paper I discuss a Bayesian approach to solving this problem that has long been available in principle but is only now becoming routinely feasible, by virtue of recent computational advances, and examine its implementation in examples that involve forecasting the price of oil and estimating the chance of catastrophic failure of the US space shuttle.