Estimation of clustering parameters using gaussian process regression.
We propose a method for estimating the clustering parameters in a Neyman-Scott Poisson process using Gaussian process regression. It is assumed that the underlying process has been observed within a number of quadrats, and from this sparse information the distribution is modelled as a Gaussian process. The clustering parameters are then estimated numerically by fitting to the covariance structure of the model. It is shown that the proposed method is resilient to any sampling regime. The method is applied to simulated two-dimensional clustered populations and the results are compared to a related method from the literature.
Australian Institute of Marine Science, Townsville, Queensland, Australia.
Paul
P
Rigby
Australian Centre for Field Robotics, The University of Sydney, Sydney, New South Wales, Australia.
Oscar
O
Pizarro
Australian Centre for Field Robotics, The University of Sydney, Sydney, New South Wales, Australia.
Stefan B
SB
Williams
D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D016011:Normal Distribution; D016012:Poisson Distribution; D012044:Regression Analysis