演講訊息 TALKS

Abstract The goal of quickest change detection in hidden Markov models (HMMs) is to utilize the observed values to detect a change from the original HMM into a new one as soon as possible, under a restriction of false alarms. Fuh and Mei (2015) studied the problem of quickest change detetion and Kullback-Leibler divergence (KL divergence) for two-state HMMs. In this short talk, I will first point out several subtle problems appeared in their paper. Next, a more general framework is developed for the multi-state HMMs and major corrections of the subtle problems are presented. Moreover, I will show that the Fredholm integral equation method proposed in Fuh and Mei (2015) is indeed an alternative way to solve random eigensystems of random matrices. To my best knowledge, constrast to the current prevailing studies on the statistical properties of random eigensystems, the integral equation approach offers a numerical approach to solving random eigensystems, which might shed some light on this topic with a better understanding. References [1] Fuh, C. D., & Mei, Y. (2015). Quickest change detection and Kullback-Leibler divergence for two-state hidden Markov models. IEEE Transactions on Signal Processing, 63(18), 4866-4878.