四种预测推断准则互不包含,揭示贝叶斯推断的适切性依赖于具体标准。
Bayes with No Shame: Admissibility Geometries of Predictive Inference
- 构建统一空间比较四类预测准则,证明彼此不可嵌套
- 在条件独立同分布模型中,后验均值是先验预测下的鞅
- 适用于关注准则间差异与贝叶斯一致性的人
现代预测系统结合了多种预测器、序列监控器、预测集和在线策略,每种都有不同的最优性证书。本文研究四种相对准则的几何结构:Blackwell风险优势、任意时间有效性可容许性、固定水平边际覆盖与声明秩索引族内的期望长度效率,以及基于选择的方法可达性(CApp)边界可行性。将四类方法嵌入共同乘积空间,证明了基于见证的成对非嵌套性:对任意一对准则类别,存在一个显式预测系统,在两个相关坐标上均活跃,属于其中一类但不满足另一类。该结果记录了不同对象空间和偏序关系下的非嵌套性,但不表示实际不相容。进一步区分三种测度相关的相干性概念。在条件独立同分布模型中,单个先验下的后验预测均值在先验预测律下是鞅。对于点零假设,e过程中的任意时间有效性可容许性等价于非负鞅性质。预测器自身的预测律下的自洽性不蕴含Blackwell可容许性,伯努利对数损失反例已证明。覆盖可容许性由声明秩索引族内的交换性秩保证,而CApp边界可行性使用Cesaro控制。受约束的贝叶斯设计框架在不坍缩其各自决策空间、偏序或风险函数的前提下整合四类范式。可容许性是准则相关的。
原文摘要 · Abstract (English)
Modern predictive systems combine predictors, sequential monitors, prediction sets, and online strategies, each with a different certificate of optimality. We study four criterion-relative geometries: Blackwell risk dominance, anytime-valid admissibility, fixed-level marginal coverage with expected-length efficiency within a declared rank-indexed family, and choice-based approachability (CApp) boundary-feasibility. We embed the four procedure types in a common product space and prove witness-based pairwise non-nesting: for every ordered pair of criterion classes, an explicit predictive system is active in both relevant coordinates, belongs to one class, and fails the other. The result records non-nesting across different object spaces and partial orders; it does not assert practical incompatibility. We separate three measure-relative coherence notions. In conditionally i.i.d. models, posterior predictive means under a single prior are martingales under the prior predictive law. For a point null, anytime-valid admissibility within e-processes is equivalent to the nonnegative martingale property. Self-consistency under a predictor's own predictive law does not imply Blackwell admissibility, as shown by a Bernoulli log-loss counterexample. Coverage admissibility is certified by exchangeability ranks within the declared rank-indexed family, while CApp boundary-feasibility uses Cesaro steering. A constrained-Bayes design schema organizes the four paradigms without collapsing their distinct decision spaces, partial orders, or risk functionals. Admissibility is criterion-relative.
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