This paper compares several statistical methods for analyzing neural feature selectivity with natural stimuli. Despite the non-Gaussian character of correlations in natural stimuli, several relevant stimulus dimensions can be found by maximizing either information or, as is demonstrated here, variance. In the case of information, the relevance of each dimension is quantified by a Kullback-Leibl...

Abstract: Positivity of the prior probability of Kullback-Leibler neighborhood around the true density, commonly known as the Kullback-Leibler property, plays a fundamental role in posterior consistency. A popular prior for Bayesian estimation is given by a Dirichlet mixture, where the kernels are chosen depending on the sample space and the class of densities to be estimated. The Kullback-Leib...

Given a collection of random variables i ] i2N where N is a nite nonempty set, the corresponding multiinformation function ascribes the relative entropy of the joint distribution of i ] i2A with respect to the product of distributions of individual random variables i for i 2 A to every subset A N. We argue it is a useful tool for problems concerning stochastic (conditional) dependence and indep...

We present an empirical investigation of various ways to automatically identify phrases in a tagged corpus that are useful for dialogue act tagging. We found that a new method (which measures a phrase’s deviation from an optimally-predictive phrase), enhanced with a lexical filtering mechanism, produces significantly better cues than manually-selected cue phrases, the exhaustive set of phrases ...

It is shown that the covariance intersection fusion rule, widely used in the context of distributed estimation, has a nice information-theoretic interpretation in terms of consensus on the Kullback-Leibler average of Gaussian probability density functions (PDFs). Based on this observation, a novel distributed state estimator based on the consensus among local posterior PDFs is proposed and its ...

Experimental data of a fluid turbulence is analysed with respect to the statistical dependency of eddies of different length scales. The joint probability distributions of velocity fluctuations of two different length scales ̧ 1 , ̧ 2 are evaluated. We quantify statistical dependency by calculating the correlation function as well as a suitably defined Kullback information.

In the paper we use two query expansion models, Kullback-Liebler Divergence(KLD) model and parameter-free Bose-Einstein statistics-based (Bo1) model, to improve effectiveness of information retrieval systems and help lay people search relevant medical information for diagnosing themselves. Compared with Baseline BM25, the results of Bo1 models with 3 feedback documents are better than baseline ...

Information entropy and its extension, which are important generalization of entropy, have been applied in many research domains today. In this paper, a novel generalized relative entropy is constructed to avoid some defects of traditional relative entropy. We presented the structure of generalized relative entropy after the discussion of defects in relative entropy. Moreover, some properties o...

We present a simple and clear foundation for finite inference that unites and significantly extends the approaches of Kolmogorov and Cox. Our approach is based on quantifying lattices of logical statements in a way that satisfies general lattice symmetries. With other applications such as measure theory in mind, our derivations assume minimal symmetries, relying on neither negation nor continui...

In this paper we exploit a volunteer-based information paradigm for archaeological aims. In particular, we present PetroAdvisor, a system supporting several fundamental activities to digitally preserve petroglyph sites. The system also uses a rewarding strategy in order to stimulate people participation to the project, so that those entering useful information gain free archaeological data, tip...