期望约束下基于Bayes定理的指数族模型
Exponential Family Models from Bayes' Theorem under Expectation Constraints
摘要：
结果表明，一致的应用贝叶斯更新从先验概率密度到后验使用证据的形式的期望约束导致完全相同的结果，作为应用的最大熵原理，即后属于指数族。本文提出的贝叶斯更新方法不是用变分原理表示的，也不涉及熵的概念。因此，它在概念上构成了一个完整的替代熵的推理方法。
英文摘要：
It is shown that a consistent application of Bayesian updating from a prior probability density to a posterior using evidence in the form of expectation constraints leads to exactly the same results as the application of the maximum entropy principle, namely a posterior belonging to the exponential family. The Bayesian updating procedure presented in this work is not expressed as a variational principle, and does not involve the concept of entropy. Therefore it conceptually constitutes a complete alternative to entropic methods of inference.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
