Statistical estimation of percolation cluster parameters
P.V. Moskalev, K.V. Grebennikov, V.V. Shitov
TL;DR
This paper introduces statistical methods for estimating parameters of the site percolation model, demonstrating their effectiveness through Monte Carlo simulations to compute confidence intervals for the fractal dimension of percolation clusters.
Contribution
It presents a novel statistical approach for parameter estimation in percolation models, including confidence interval computation for fractal dimensions.
Findings
Effective estimation of cluster parameters using proposed methods
Monte Carlo simulations validate the confidence intervals
Improved accuracy in fractal dimension measurement
Abstract
In this paper we study statistical methods of parameters estimation of the site percolation model. Advantages of the proposed method is demonstrated for the computing of the confidence interval of mass fractal dimension of a percolation clusters sampling, formed by the Monte Carlo method.
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Taxonomy
TopicsAdvanced Clustering Algorithms Research · Bayesian Methods and Mixture Models · Advanced Scientific Research Methods