Decisions with Limited Observations over a Finite Product Space: the Klir Effect
This addresses decision-making under uncertainty for applications with sparse data, but appears incremental as it builds on existing maximum entropy methods.
The paper tackles the problem of making decisions with limited observations over a finite product space by using maximum entropy reconstruction of probability estimates from conditional independence relations, and finds that this technique may improve decision quality.
Probability estimation by maximum entropy reconstruction of an initial relative frequency estimate from its projection onto a hypergraph model of the approximate conditional independence relations exhibited by it is investigated. The results of this study suggest that use of this estimation technique may improve the quality of decisions that must be made on the basis of limited observations over a decomposable finite product space.