On the Bayesian estimation for the stationary Neyman-Scott point processes
Applications of Mathematics (2016)
- Volume: 61, Issue: 4, page 503-514
- ISSN: 0862-7940
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topKopecký, Jiří, and Mrkvička, Tomáš. "On the Bayesian estimation for the stationary Neyman-Scott point processes." Applications of Mathematics 61.4 (2016): 503-514. <http://eudml.org/doc/283405>.
@article{Kopecký2016,
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.},
author = {Kopecký, Jiří, Mrkvička, Tomáš},
journal = {Applications of Mathematics},
keywords = {Bayesian method; Monte Carlo Markov chain; Neyman-Scott point process; parameter estimation; shot-noise Cox process; Thomas process; Bayesian method; Monte Carlo Markov chain; Neyman-Scott point process; parameter estimation; shot-noise Cox process; Thomas process},
language = {eng},
number = {4},
pages = {503-514},
publisher = {Institute of Mathematics, Academy of Sciences of the Czech Republic},
title = {On the Bayesian estimation for the stationary Neyman-Scott point processes},
url = {http://eudml.org/doc/283405},
volume = {61},
year = {2016},
}
TY - JOUR
AU - Kopecký, Jiří
AU - Mrkvička, Tomáš
TI - On the Bayesian estimation for the stationary Neyman-Scott point processes
JO - Applications of Mathematics
PY - 2016
PB - Institute of Mathematics, Academy of Sciences of the Czech Republic
VL - 61
IS - 4
SP - 503
EP - 514
AB - 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.
LA - eng
KW - Bayesian method; Monte Carlo Markov chain; Neyman-Scott point process; parameter estimation; shot-noise Cox process; Thomas process; Bayesian method; Monte Carlo Markov chain; Neyman-Scott point process; parameter estimation; shot-noise Cox process; Thomas process
UR - http://eudml.org/doc/283405
ER -
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