吉布斯条件化的扩展，玻尔兹曼条件化的引入
Gibbs conditioning extended, Boltzmann conditioning introduced
摘要：
I-投影上类型的条件等浓度（ICET）和扩展吉布斯条件原理（EGCP）将条件弱大数定律和吉布斯条件原理推广到非唯一相对熵最大化（REM）分布（又称I-投影）的情形。ICET和EGCP在相当一般的条件下给出了REM的概率证明。引入结果的μ投影变体。它们为最大概率（MaxProb）方法提供了一个概率性的证明。REM/MaxEnt还是MaxProb？”的问题进行了讨论，简要。Jeffreys Conditioning Principle是什么意思
英文摘要：
Conditional Equi-concentration of Types on I-projections (ICET) and Extended Gibbs Conditioning Principle (EGCP) provide an extension of Conditioned Weak Law of Large Numbers and of Gibbs Conditioning Principle to the case of non-unique Relative Entropy Maximizing (REM) distribution (aka I-projection). ICET and EGCP give a probabilistic justification to REM under rather general conditions. mu-projection variants of the results are introduced. They provide a probabilistic justification to Maximum Probability (MaxProb) method. 'REM/MaxEnt or MaxProb?' question is discussed, briefly. Jeffreys Conditioning Principle is mentioned.
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。
