The regional and provincial divergence in China’s economic growth has attracted extensive and intensive attention, yet the measurement and research on the sources of the divergence is relatively inadequate. Why there is divergence across provinces? What elements have contributed to the divergence and how? Based on growth regression analysis and “counterfactual econometrics”, this paper construc...

We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which grea...

Classifier fusion is a means to increase accuracy and decision-making of classification systems by designing a set of basis classifiers and then combining their outputs. The combination is made up by non linear functional dependent on fuzzy measures called Choquet integral. It constitues a vast family of aggregation operators including minimum, maximum or weighted sum. The main issue before app...

In this paper we address the task of image categorization using a new similarity measure on the space of Sparse Multiscale Patches (SMP). SMPs are based on a multiscale transform of the image and provide a global representation of its content. At each scale, the probability density function (pdf ) of the SMPs is used as a description of the relevant information. The closeness between two images...

In this work we present two new methods for approximating the Kullback-Liebler (KL) divergence between two mixtures of Gaussians. The first method is based on matching between the Gaussian elements of the two Gaussian mixture densities. The second method is based on the unscented transform. The proposed methods are utilized for image retrieval tasks. Continuous probabilistic image modeling base...

This paper presents a new similarity measure based on Rao distance for color texture classification or retrieval. Textures are characterized by a joint model of complex wavelet coefficients. This model is based on a Gaussian Copula in order to consider the dependency between color components. Then, a closed form of Rao distance is computed to measure the difference between two Gaussian Copula b...

Density estimation, especiallymultivariate density estimation, is a fundamental problem in nonparametric inference. In the Bayesian approach, Dirichlet mixture priors are often used in practice for such problems. However, the asymptotic properties of such priors have only been studied in the univariate case. We extend the L1-consistency of Dirichlet mixutures in the multivariate density estimat...

We define a new notion of fairness for term rewriting system (TRS). We prove the modularity of termination of TRS under such fair strategies, that is, two TRS terminate under fair strategies if and only if their disjoint union terminates under fair strategies. In order to do so, we demonstrate that termination under fair strategies of a TRS is equivalent to the TRS being weakly terminating and ...

Should the regime-switching risk be priced? This is perhaps one of the important “normative” issues to be addressed in pricing contingent claims under a Markovian, regime-switching, BlackScholes-Merton model. We address this issue using a minimal relative entropy approach. Firstly, we apply a martingale representation for a double martingale to characterize the canonical space of equivalent mar...

Density estimation, especially multivariate density estimation, is a fundamental problem in nonparametric inference. Dirichlet mixture priors are often used in practice for such problem. However, asymptotic properties of such priors have only been studied in the univariate case. We extend L1-consistency of Dirichlet mixutures in the multivariate density estimation setting. We obtain such a resu...