On the Bayesian estimation for the stationary Neyman-Scott point processes

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...

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Bibliographic Details
Published in:Applications of mathematics (Prague) 2016-08, Vol.61 (4), p.503-514
Main Authors: Kopecký, Jirí, Mrkvicka, Tomás
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Applications of Mathematics
Bayesian analysis
Classical and Continuum Physics
Computer simulation
Error analysis
Estimates
Estimating techniques
Markov analysis
Mathematical analysis
Mathematical and Computational Engineering
Mathematical and Computational Physics
Mathematics
Mathematics and Statistics
Mean square values
Methods
Monte Carlo simulation
Optimization
Parameter estimation
Parameter modification
Simulation
Studies
Theoretical
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Summary: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.
ISSN:0862-7940
1572-9109
DOI:10.1007/s10492-016-0144-8