论文标题
在熵倾斜和预测条件上
On Entropic Tilting and Predictive Conditioning
论文作者
论文摘要
熵倾斜（ET）是一种贝叶斯决策分析方法，用于限制分布以满足定义目标或界限的期望集。该报告概括了基础和基本理论，用于根据此类约束来调节预测分布的基础和基本理论，从而认识到几个应用领域对ET的兴趣日益增加。贡献包括与定期指数分布族的连接有关的新结果，以及将ET扩展到放松的熵倾斜（RET）的情况下，指定的期望值定义了界限，而不是确切的目标。其他新的发展包括理论和示例，这些理论和示例是针对修改的预测分布的分位数约束，以及与贝叶斯预测应用相关的示例。
Entropic tilting (ET) is a Bayesian decision-analytic method for constraining distributions to satisfy defined targets or bounds for sets of expectations. This report recapitulates the foundations and basic theory of ET for conditioning predictive distributions on such constraints, recognising the increasing interest in ET in several application areas. Contributions include new results related to connections with regular exponential families of distributions, and the extension of ET to relaxed entropic tilting (RET) where specified values for expectations define bounds rather than exact targets. Additional new developments include theory and examples that condition on quantile constraints for modified predictive distributions and examples relevant to Bayesian forecasting applications.
