We propose a parametric version of Independent Component Analysis (ICA) via Copulas families of multivariate distributions that join univariate margins to multivariate distributions. Our procedure exploits the role for copula models in information theory and in measures of association, specifically: the use of copulae densities as parametric mutual information, and as measures of association on...

Do Students Expect Compensation for Wage Risk? We use a unique data set about the wage distribution that Swiss students expect for themselves ex ante, deriving parametric and non-parametric measures to capture expected wage risk. These wage risk measures are unfettered by heterogeneity which handicapped the use of actual market wage dispersion as risk measure in earlier studies. Students in our...

In the context of computer vision, matching can be done with similarity measures. This paper presents the classification of these measures into five families. In addition, eighteen measures based on robust statistics, previously proposed [1] in order to deal with the problem of occlusions, are studied and compared to the state of the art. A new evaluation protocol and new analyses are proposed ...

The probabilistic framework of extreme value theory is well-known: the dependence structure of large events is characterized by an angular measure on the positive orthant of the unit sphere. The family of these angular measures is non-parametric by nature. Nonetheless, any angular measure may be approached arbitrarily well by a mixture of Dirichlet distributions. The semi-parametric Dirichlet m...

We propose a texture similarity measure based on the Kullback-Leibler divergence between gamma distributions (KLGamma). We conjecture that the spatially smoothed Gabor filter magnitude responses of some classes of visually homogeneous stochastic textures are gamma distributed. Classification experiments with disjoint test and training images, show that the KLGamma measure performs better than o...

Automated configuration procedures play an increasingly prominent role in realising the performance potential inherent in highly parametric solvers for a wide range of computationally challenging problems. However, these configuration procedures have difficulties when dealing with inhomogenous instance sets, where the relative difficulty of problem instances varies between configurations of the...

In this article our objective is to evaluate the performance of different measures of associations for hypothesis testing purposes. We have considered different measures of association (including some commonly used) in this study, one of which is parametric and others are non-parametric including three proposed modifications. Performance of these tests are compared under different symmetric, sk...

This paper introduces a novel way of adapting the Hybrid Monte Carlo (HMC) algorithm using parametric bandits with nonlinear features. HMC is a powerful Markov chain Monte Carlo (MCMC) method, but it requires careful tuning of its hyper-parameters. We propose a Bayesian parametric bandit approach to carry out the adaptation of the hyper-parameters while the Markov chain progresses. We also intr...

We present a technique to extend any distortion (quality) measure for planar meshes to meshes on parameterized surfaces. The resulting distortion (quality) measure is expressed in terms of the parametric coordinates of the nodes. This extended distortion (quality) measure can be used to check the quality and validity of a surface mesh. We also apply it to simultaneously smooth and untangle surf...