4,573 research outputs found
Jensen-Shannon divergence (JS) between topics of different methods.
No full textJensen-Shannon divergence (JS) between topics of different methods.</p
On a generalization of the Jensen-Shannon divergence and the JS-symmetrization of distances relying on abstract means
No full textThe Jensen-Shannon divergence is a renown bounded symmetrization of the
unbounded Kullback-Leibler divergence which measures the total Kullback-Leibler
divergence to the average mixture distribution. However the Jensen-Shannon
divergence between Gaussian distributions is not available in closed-form. To
bypass this problem, we present a generalization of the Jensen-Shannon (JS)
divergence using abstract means which yields closed-form expressions when the
mean is chosen according to the parametric family of distributions. More
generally, we define the JS-symmetrizations of any distance using generalized
statistical mixtures derived from abstract means. In particular, we first show
that the geometric mean is well-suited for exponential families, and report two
closed-form formula for (i) the geometric Jensen-Shannon divergence between
probability densities of the same exponential family, and (ii) the geometric
JS-symmetrization of the reverse Kullback-Leibler divergence. As a second
illustrating example, we show that the harmonic mean is well-suited for the
scale Cauchy distributions, and report a closed-form formula for the harmonic
Jensen-Shannon divergence between scale Cauchy distributions. We also define
generalized Jensen-Shannon divergences between matrices (e.g., quantum
Jensen-Shannon divergences) and consider clustering with respect to these novel
Jensen-Shannon divergences.Comment: 33 page
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JS-MA: A Jensen-Shannon Divergence Based Method for Mapping Genome-Wide Associations on Multiple Diseases
No full textArticle develops a a simple, fast, and powerful method, named JS-MA, based on Jensen-Shannon divergence and agglomerative hierarchical clustering, to detect the genome-wide multi-locus interactions associated with multiple diseases
Heatmap showing the Jensen–Shannon (JS) divergence between challenge groups estimated from FPKM values for all genes.
No full text<p>Heatmap showing the Jensen–Shannon (JS) divergence between challenge groups estimated from FPKM values for all genes.</p
Learning With Jensen-Tsallis Kernels
No full textJensen-type Jensen-Shannon (JS) and Jensen-Tsallis] kernels were first proposed by Martins et al. (2009). These kernels are based on JS divergences that originated in the information theory. In this paper, we extend the Jensen-type kernels on probability measures to define positive-definite kernels on Euclidean space. We show that the special cases of these kernels include dot-product kernels. Since Jensen-type divergences are multidistribution divergences, we propose their multipoint variants, and study spectral clustering and kernel methods based on these. We also provide experimental studies on benchmark image database and gene expression database that show the benefits of the proposed kernels compared with the existing kernels. The experiments on clustering also demonstrate the use of constructing multipoint similarities
On the Jensen–Shannon Symmetrization of Distances Relying on Abstract Means
No full textThe Jensen–Shannon divergence is a renowned bounded symmetrization of the unbounded Kullback–Leibler divergence which measures the total Kullback–Leibler divergence to the average mixture distribution. However, the Jensen–Shannon divergence between Gaussian distributions is not available in closed form. To bypass this problem, we present a generalization of the Jensen–Shannon (JS) divergence using abstract means which yields closed-form expressions when the mean is chosen according to the parametric family of distributions. More generally, we define the JS-symmetrizations of any distance using parameter mixtures derived from abstract means. In particular, we first show that the geometric mean is well-suited for exponential families, and report two closed-form formula for (i) the geometric Jensen–Shannon divergence between probability densities of the same exponential family; and (ii) the geometric JS-symmetrization of the reverse Kullback–Leibler divergence between probability densities of the same exponential family. As a second illustrating example, we show that the harmonic mean is well-suited for the scale Cauchy distributions, and report a closed-form formula for the harmonic Jensen–Shannon divergence between scale Cauchy distributions. Applications to clustering with respect to these novel Jensen–Shannon divergences are touched upon
Jensen-Shannon divergence and cosine follow a similar pattern to the perplexity.
No full textJS increases from 10 (higher is better) and cosine decreases from 10 (lower is better). (TIF)</p
Chosen logistics processes in Škoda JS
No full textThis master thesis deals with the purchase and sale process in Škoda JS company. The aim of this work is to assess whether the setting of the purchase and sale process is met by the company also within a real business case, in compliance with set controls, and whether the degree of perfect delivery is sufficient. In the introduction, the author specifies the basic terms: logistics, logistic chain, customer benefits, information systems in logistics, buying and selling. The following chapter introduces Škoda JS company, including the sphere of its entrepreneurial activity. This chapter also deals with the nuclear power industry. In the crucial chapter, the author describes the process of purchase and sale in Škoda JS company and compares it with a real business case. In conclusion, the author evaluates discrepancies and suggests recommendations to avoid them
Conceptualizing Surprise with the Jensen–Shannon distance: A Bayesian Information Theoretical Approach for the Social Sciences
No full textGenerally in the social sciences, a result is informally deemed ‘surprising’ if its associated p-value is sufficiently small. This implicit interpretation, however, is both conceptually and mathematically inappropriate, and such misuse of the p-value can lead to erroneous conclusions about the novelty of results. To solve that issue, this paper builds on Bayesian inference, Information theory, and considerations specific to the social sciences, to argue for the adoption of a more appropriate conceptualization of surprise as the relative entropy between prior and posterior knowledge. Novel to the social sciences, this formal conceptualization enables researchers to appropriately measure surprise as the Jensen-Shannon distance, for which the paper contributes with easily implementable software and a demonstration of its use in relation to empirical data.©
Keywords: Surprise; Novelty; Jensen-Shannon distance; JS distance; Jensen-Shannon divergence; JS divergence; Relative entropy; Kullback-Leibler divergence; KL divergence; Distance; Divergence; Dissimilarity; Differential entropy; Entropy; p-value; S-value; Information theory; Bayesian
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