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Keywords:

  • scale space smoothing;
  • Bayesian methods;
  • scatter plots;
  • random fields;
  • climate research

Abstract

BSiZer is a smoothing-based Bayesian data analysis tool that can be used to find scale-dependent features in scatter plots. It uses a scale space approach which means that, instead of just one smooth, a whole family of smoothing levels are explored, with each level thought to describe the object of interest at a particular scale or resolution. While the original BSiZer was used to analyze curves underlying noisy data, we discuss also its subsequent extensions to the analysis of images and random fields. The methodology is demonstrated with applications to climate reconstruction and prediction and image analysis. © 2010 John Wiley & Sons, Inc.

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