Quickest change detection and Kullback-Leibler divergence for two-state hidden Markov models

The quickest change detection problem is studied in two-state hidden Markov models (HMM), where the vector parameter θ of the HMM may change from θ
₀
to θ
₁
at some unknown time, and one wants to detect the true change as quickly as possible while controlling the false alarm rate. It turns out that the generalized likelihood ratio (GLR) scheme, while theoretically straightforward, is generally computationally infeasible for the HMM. To develop efficient but computationally simple schemes for the HMM, we first show that the recursive CUSUM scheme proposed in Fuh (Ann. Statist., 2003) can be regarded as a quasi-GLR scheme for some suitable pseudo post-change hypotheses. Next, we extend the quasi-GLR idea to propose recursive score schemes in a more complicated scenario when the post-change parameter θ
₁
of the HMM involves a real-valued nuisance parameter. Finally, our research provides an alternative approach that can numerically compute the Kullback-Leibler (KL) divergence of two-state HMMs via the invariant probability measure and the Fredholm integral equation.