1,630 research outputs found
On the average uncertainty for systems with nonlinear coupling
The increased uncertainty and complexity of nonlinear systems have motivated
investigators to consider generalized approaches to defining an entropy
function. New insights are achieved by defining the average uncertainty in the
probability domain as a transformation of entropy functions. The Shannon
entropy when transformed to the probability domain is the weighted geometric
mean of the probabilities. For the exponential and Gaussian distributions, we
show that the weighted geometric mean of the distribution is equal to the
density of the distribution at the location plus the scale, i.e. at the width
of the distribution. The average uncertainty is generalized via the weighted
generalized mean, in which the moment is a function of the nonlinear source.
Both the Renyi and Tsallis entropies transform to this definition of the
generalized average uncertainty in the probability domain. For the generalized
Pareto and Student's t-distributions, which are the maximum entropy
distributions for these generalized entropies, the appropriate weighted
generalized mean also equals the density of the distribution at the location
plus scale. A coupled entropy function is proposed, which is equal to the
normalized Tsallis entropy divided by one plus the coupling.Comment: 24 pages, including 4 figures and 1 tabl
Direct Estimation of Information Divergence Using Nearest Neighbor Ratios
We propose a direct estimation method for R\'{e}nyi and f-divergence measures
based on a new graph theoretical interpretation. Suppose that we are given two
sample sets and , respectively with and samples, where
is a constant value. Considering the -nearest neighbor (-NN)
graph of in the joint data set , we show that the average powered
ratio of the number of points to the number of points among all -NN
points is proportional to R\'{e}nyi divergence of and densities. A
similar method can also be used to estimate f-divergence measures. We derive
bias and variance rates, and show that for the class of -H\"{o}lder
smooth functions, the estimator achieves the MSE rate of
. Furthermore, by using a weighted ensemble
estimation technique, for density functions with continuous and bounded
derivatives of up to the order , and some extra conditions at the support
set boundary, we derive an ensemble estimator that achieves the parametric MSE
rate of . Our estimators are more computationally tractable than other
competing estimators, which makes them appealing in many practical
applications.Comment: 2017 IEEE International Symposium on Information Theory (ISIT
Reduced perplexity: Uncertainty measures without entropy
Conference paper presented at Recent Advances in Info-Metrics, Washington, DC, 2014. Under review for a book chapter in "Recent innovations in info-metrics: a cross-disciplinary perspective on information and information processing" by Oxford University Press.A simple, intuitive approach to the assessment of probabilistic inferences is introduced. The Shannon information metrics are translated to the probability domain. The translation shows that the negative logarithmic score and the geometric mean are equivalent measures of the accuracy of a probabilistic inference. Thus there is both a quantitative reduction in perplexity as good inference algorithms reduce the uncertainty and a qualitative reduction due to the increased clarity between the original set of inferences and their average, the geometric mean. Further insight is provided by showing that the Renyi and Tsallis entropy functions translated to the probability domain are both the weighted generalized mean of the distribution. The generalized mean of probabilistic inferences forms a Risk Profile of the performance. The arithmetic mean is used to measure the decisiveness, while the -2/3 mean is used to measure the robustness
Source coding with escort distributions and Renyi entropy bounds
We discuss the interest of escort distributions and R\'enyi entropy in the
context of source coding. We first recall a source coding theorem by Campbell
relating a generalized measure of length to the R\'enyi-Tsallis entropy. We
show that the associated optimal codes can be obtained using considerations on
escort-distributions. We propose a new family of measure of length involving
escort-distributions and we show that these generalized lengths are also
bounded below by the R\'enyi entropy. Furthermore, we obtain that the standard
Shannon codes lengths are optimum for the new generalized lengths measures,
whatever the entropic index. Finally, we show that there exists in this setting
an interplay between standard and escort distributions
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