2020/08/31 by Jason Ford, Ford, Jason J., Jasmin James +3
Computer Science · Decision Sciences · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Data Quality and Management #FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.00150
openalex publication_date 2020/08/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.