Spatial prediction of categorical variables: the Bayesian maximum entropy approach

By Patrick Bogaert. 2002

87 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/4bcb1d9b326f304a0ca08902d5d73b6de577ed8c

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Citations in the corpus

The spatial allocation of population: a review of large-scale gridded population data products and their fitness for use (2019, background): “…mapping and can be viewed as another means of population estimation, traditionally focusing on the problem of small area estimation (e.g., Birkin and Clarke, 1988; Wong, 1992; Bogaert, 2002), or as a type of dasymetric refinement (e.g., Mrozinski and Cromley, 1999; Leyk et al., 2013) (Fig.”
Levenberg–Marquardt forms of the iterative ensemble smoother for efficient history matching and uncertainty quantification (2013, background): “Like Bogaert [3], we emphasize that the goal of our study is not to correctly estimate the true value of the variables, but rather to estimate expectations from the conditional probability.”
An efficient maximum entropy approach for categorical variable prediction (2011, methodology): “MCP, (Equation (7)) is thus an approximation to the full BME solution as given by D’Or & Bogaert (2004). It is, however, fast, easy to code and, in most cases, a very accurate approximation as shown later.”
Space-time mapping of soil salinity using probabilistic bayesian maximum entropy (2004, background): “(2001), Bogaert and D’Or (2002), and D’Or and Bogaert (2003) mapped soil texture using BME in the space domain. Serre and Christakos (1999) studied the water-table elevations of an aquifer in Kansas while Christakos and Serre (2000) analyzed the distribution of particulate matter in North Carolina. ”
Bayesian Maximum Entropy prediction of soil categories using a traditional soil map as soft information (2008, methodology): “Hence positive definiteness of the probability model is not an issue here, and we can rely on nonparametric modelling using, e.g., a kernel smoothing procedure, instead of restricting ourselves to a limited class of authorized models (Bogaert, 2002).”