Software

New: Matlab suite for D-dimensional interpolation using the stochastic local interaction model

Machine learning and geostatistics are powerful mathematical frameworks for modeling spatial data. Both approaches, however, suffer from poor scaling of the required computational resources for large data applications. We present the Stochastic Local Interaction (SLI) model, which employs a local representation to improve computational efficiency. SLI combines geostatistics and machine learning with ideas from statistical physics and computational geometry. It is based on a joint probability density function defined by an energy functional which involves local interactions implemented by means of kernel functions with adaptive local kernel bandwidths. SLI is expressed in terms of an explicit, typically sparse, precision (inverse covariance) matrix. This representation leads to a semi-analytical expression for interpolation (prediction), which is valid in any number of dimensions and avoids the computationally costly covariance matrix inversion.

The software below has been tested on the Windows 7 platform with the Matlab releases R2013a and R2015a. For comments and questions email Dionisis : dionisi at mred.tuc.gr

Matlab software for SLI model (April 2015):

The SLI model is published in the special issue on Statistical Learning in Geoscience in the journal Computers & Geosciences Volume 85, Part B, December 2015, Pages 26–37.

doi: http://dx.doi.org/10.1016/j.cageo.2015.05.018

A preprint of this article is posted at: arxiv.org/abs/1501.04053