利用测度变换和渐近马氏链的强逆
Strong Converses using Change of Measure and Asymptotic Markov Chains
摘要：
本文的主要贡献是一个强大的逆结果$K$跳分布式假设检验独立多（中间）决策中心下的马尔可夫条件。我们的结果表明，可以同时实现在所有终端的第二类错误指数的集合不依赖于最大允许的第一类错误概率。我们的强逆证明是基于测量参数的变化和特定的马尔可夫链的渐近证明。这种证明方法也可以用于其他逆证明，是有吸引力的，因为它不需要诉诸变分特征或爆破方法，在以前的相关证明。
英文摘要：
The main contribution of this paper is a strong converse result for $K$-hop distributed hypothesis testing against independence with multiple (intermediate) decision centers under a Markov condition. Our result shows that the set of type-II error exponents that can simultaneously be achieved at all the terminals does not depend on the maximum permissible type-I error probabilities. Our strong converse proof is based on a change of measure argument and on the asymptotic proof of specific Markov chains. This proof method can also be used for other converse proofs, and is appealing because it does not require resorting to variational characterizations or blowing-up methods as in previous related proofs.
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。
