论文标题
完全最佳的贝叶斯最佳变化检测是隐藏的马尔可夫模型
Exactly Optimal Bayesian Quickest Change Detection for Hidden Markov Models
论文作者
论文摘要
本文考虑了贝叶斯环境中隐藏的马尔可夫模型（HMM）的最快检测问题。我们构建了问题的增强HMM表示，该表示允许采用动态编程方法来证明Shiryaev的规则是（确切的）最佳解决方案。这种增强的表示强调了该问题的基本信息结构，并提出了可能放松，以使更多异国情调的事件先验未出现在文献中。最后，这种增强表示形式还使我们能够提出一种实现最佳解决方案的有效计算方法。
This paper considers the quickest detection problem for hidden Markov models (HMMs) in a Bayesian setting. We construct an augmented HMM representation of the problem that allows the application of a dynamic programming approach to prove that Shiryaev's rule is an (exact) optimal solution. This augmented representation highlights the problem's fundamental information structure and suggests possible relaxations to more exotic change event priors not appearing in the literature. Finally, this augmented representation also allows us to present an efficient computational method for implementing the optimal solution.
