Abstract
We propose to apply the importance sampling and the antithetic variates statistical techniques to estimate steady-state quantities of an Hidden Markov chain (HMM) of which certain emissions are rarely generated. Compared to standard Monte Carlo simulation, the use of these techniques, allow a significant reduction in simulation time. Numerical Monte Carlo examples are studied to show the usefulness and efficiency of the proposed approach.
Acknowledgements
The authors wish to express their sincere gratitude to the Editor-in-Chief for following up on the paper, and gratefully acknowledge the many helpful suggestions and valuable comments of the Reviewers that lead to an improved paper.
References
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© 2022 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- A study of highly efficient stochastic sequences for multidimensional sensitivity analysis
- A note on the asymptotic stability of the semi-discrete method for stochastic differential equations
- Estimation of steady-state quantities of an HMM with some rarely generated emissions
- Recursive regression estimation based on the two-time-scale stochastic approximation method and Bernstein polynomials
- Unbiased estimation of the gradient of the log-likelihood for a class of continuous-time state-space models
- Simulation of Gaussian random field in a ball
Artikel in diesem Heft
- Frontmatter
- A study of highly efficient stochastic sequences for multidimensional sensitivity analysis
- A note on the asymptotic stability of the semi-discrete method for stochastic differential equations
- Estimation of steady-state quantities of an HMM with some rarely generated emissions
- Recursive regression estimation based on the two-time-scale stochastic approximation method and Bernstein polynomials
- Unbiased estimation of the gradient of the log-likelihood for a class of continuous-time state-space models
- Simulation of Gaussian random field in a ball