Abstract
The pure and modified Bayesian methods are applied to the estimation of parameters of the Neyman-Scott point process. Their performance is compared to the fast, simulation-free methods via extensive simulation study. Our modified Bayesian method is found to be on average 2.8 times more accurate than the fast methods in the relative mean square errors of the point estimates, where the average is taken over all studied cases. The pure Bayesian method is found to be approximately as good as the fast methods. These methods are computationally affordable today.
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This work was funded by the Grant Agency of the Czech Republic (Project No. 16-03708S).
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Kopecký, J., Mrkvička, T. On the Bayesian estimation for the stationary Neyman-Scott point processes. Appl Math 61, 503–514 (2016). https://doi.org/10.1007/s10492-016-0144-8
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DOI: https://doi.org/10.1007/s10492-016-0144-8
Keywords
- Bayesian method
- Monte Carlo Markov chain
- Neyman-Scott point process
- parameter estimation
- shot-noise Cox process
- Thomas process