A Pessimistic Approximation for the Fisher Information Measure

Relations between Renyi Distance and Fisher Information

In this paper, we first show that Renyi distance between any member of a parametric family and its perturbations, is proportional to its Fisher information. We, then, prove some relations between the Renyi distance of two distributions and the Fisher information of their exponentially twisted family of densities. Finally, we show that the partial ordering of families induced by Renyi dis...

On an Information Theoretic Approximation Measure for Functional Dependencies

We investigate the problem of de ning an approximation measure for functional dependencies (FDs). For xed sets of attributes, X and Y , an approximation measure is a function which maps relation instances to real numbers. The number to which an instance is mapped, intuitively, describes the strength of the dependency, X ! Y , in that instance. We de ne an approximation measure for FDs based on ...

Model selection by minimum description length: Lower-bound sample sizes for the Fisher information approximation

The Fisher information approximation (FIA) is an implementation of the minimum description length principle for model selection. Unlike information criteria such as AIC or BIC, it has the advantage of taking the functional form of amodel into account. Unfortunately, FIA can bemisleading in finite samples, resulting in an inversion of the correct rank order of complexity terms for competing mode...

The MDF discrimination measure: Fisher in disguise

Recently, a discrimination measure for feature extraction for two-class data, called the maximum discriminating (MDF) measure (Talukder and Casasent [Neural Networks 14 (2001) 1201-1218]), was introduced. In the present paper, it is shown that the MDF discrimination measure produces exactly the same results as the classical Fisher criterion, on the condition that the two prior probabilities are...

A Measure for Ve tor Approximation