Estimation of clustering parameters using gaussian process regression

Paul Rigby; Oscar Pizarro; Stefan B Williams

doi:10.1371/journal.pone.0111522

Estimation of clustering parameters using gaussian process regression

PLoS One. 2014 Nov 10;9(11):e111522. doi: 10.1371/journal.pone.0111522. eCollection 2014.

Authors

Paul Rigby¹, Oscar Pizarro², Stefan B Williams²

Affiliations

¹ Australian Institute of Marine Science, Townsville, Queensland, Australia.
² Australian Centre for Field Robotics, The University of Sydney, Sydney, New South Wales, Australia.

Abstract

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.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cluster Analysis*
Data Interpretation, Statistical*
Models, Statistical*
Normal Distribution
Poisson Distribution
Regression Analysis

Grants and funding

This work is funded by the Australian Research Council (ARC), the New South Wales State Government, and the Integrated Marine Observing System (IMOS) through the National Collaborative Research Infrastructure Strategy and the Super Science Initiative. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.