An efficient maximum entropy approach for categorical variable prediction

By Denis Allard, D. D’Or, R. Froidevaux. 2011

53 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ce76f1834f16834ea30b6d16423b133e73ce7575

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philippe Renard
2. Alessandro Comunian
3. Denis Allard
4. Xavier Emery
5. Roussos G. Dimitrakopoulos
6. Philippe Naveau
7. William Kleiber
8. Michael L. Stein

In Signal Processing

1. Alessandro Comunian
2. Philippe Renard
3. Denis Allard

Citations in the corpus

• Probability Aggregation Methods in Geoscience (2012, methodology): “Allard et al. (2011) developed such an approach for the prediction of spatial categorical variables leading to a Markovian-type categorical prediction (MCP), which was shown to be a very good approximation of the Bayesian maximum entropy (BME) principle (Christakos 1990) with the advantage of being…”