Abstract Bayesian Maximum Entropy (BME) has been successfully used in geostatistics to
calculate predictions of spatial variables given some general knowledge base and sets of …

The Bayesian maximum entropy (BME) method can be used to predict the value of a spatial
random field at an unsampled location given precise (hard) and imprecise (soft) data. It has …

Due to the fast pace increasing availability and diversity of information sources in
environmental sciences, there is a real need of sound statistical mapping techniques for …

The Bayesian maximum entropy (BME) method is a valuable tool, with rigorous theoretical
underpinnings, with which to predict with soft (imprecise) data. The methodology uses a …

Geostatistics should not be considered merely as a set of mathematical techniques for
semivariogram calculation, spatial estimation and simulation, on the basis of a (usually …

Being a non-linear method based on a rigorous formalism and an efficient processing of
various information sources, the Bayesian maximum entropy (BME) approach has proven to …

Soil properties play important roles in a lot of environmental issues like diffuse pollution,
erosion hazards or precision agriculture. With the developments of soil process models and …

Abstract The Bayesian Maximum Entropy (BME) method of spatial analysis and mapping
provides definite rules for incorporating prior information, hard and soft data into the …

The present paper reviews the conceptual framework and development of the Bayesian
Maximum Entropy (BME) approach. BME has been considered as a significant breakthrough …

This paper is concerned with a computational formulation of the Bayesian maximum entropy
(BME) mapping method, which can handle rigorously and efficiently spatiotemporal …