Simulation study demonstrates that importance sampling accelerates steady-state rare event estimation in Hidden Markov Models, indicating improved efficiency over standard Monte Carlo.
Importance sampling is utilized to enhance the Hidden Markov Models’ rare events estimation, yielding results rapidly and accurately than with standard Monte Carlo methods. We study the estimation of the steady state quantities of a Hidden Markov Model with rare events, by using the Importance Sampling technique. When compared to standard Monte Carlo simulations, this method offer a notable reduction in simulation time. The effectiveness and practicality of the approach are illustrated through a basic numerical example.
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Kroumbi et al. (2026) studied this question.
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