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Total Variation Distance
"... Abstract—We give explicit expressions, upper and lower bounds on the total variation distance between P and Q in terms of the distribution of the random variables log dP dQ (X) and log dP dQ (Y), where X and Y are distributed according to P and Q respectively. I. ..."
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Abstract—We give explicit expressions, upper and lower bounds on the total variation distance between P and Q in terms of the distribution of the random variables log dP dQ (X) and log dP dQ (Y), where X and Y are distributed according to P and Q respectively. I.
ON THE VARIATIONAL DISTANCE OF TWO TREES 1
"... A widely studied model for generating sequences is to “evolve ” them on a tree according to a symmetric Markov process. We prove that model trees tend to be maximally “far apart ” in terms of variational distance. 1. Introduction. In ..."
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A widely studied model for generating sequences is to “evolve ” them on a tree according to a symmetric Markov process. We prove that model trees tend to be maximally “far apart ” in terms of variational distance. 1. Introduction. In
On the variational distance of independently repeated experiments
, 2005
"... Let P and Q be two probability distributions which differ only for values with probability at least p> 0. We show that the variational distance δ(P n, Q n) between the nfold product distributions P n and Q n is upper bounded by √ n/(2p)δ(P, Q), i.e., it cannot grow faster than the square root of ..."
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Let P and Q be two probability distributions which differ only for values with probability at least p> 0. We show that the variational distance δ(P n, Q n) between the nfold product distributions P n and Q n is upper bounded by √ n/(2p)δ(P, Q), i.e., it cannot grow faster than the square root
Fairness Index Based on Variational Distance
"... Abstract — Fairness index among competing hosts in communication networks is an important system measurement. Several fairness index measurements have been proposed in the technical literature. However, most of these measurements, such as the max/min fairness index and Jain’s index, reflect only a l ..."
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on Variational Distance (FIVD), and related fairness indices are presented to show the benefit of our measurement. I.
Divergence measures based on the Shannon entropy
 IEEE Transactions on Information theory
, 1991
"... AbstractA new class of informationtheoretic divergence measures based on the Shannon entropy is introduced. Unlike the wellknown Kullback divergences, the new measures do not require the condition of absolute continuity to be satisfied by the probability distributions involved. More importantly, ..."
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Cited by 666 (0 self)
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, their close relationship with the variational distance and the probability of misclassification error are established in terms of bounds. These bounds are crucial in many applications of divergence measures. The new measures are also well characterized by the properties of nonnegativity, finiteness
Trends to equilibrium in total variation distance
, 2008
"... This paper presents different approaches, based on functional inequalities, to study the speed of convergence in total variation distance of ergodic diffusion processes with initial law satisfying a given integrability condition. To this end, we give a general upper bound “à la Pinsker” enabling u ..."
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Cited by 1 (1 self)
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This paper presents different approaches, based on functional inequalities, to study the speed of convergence in total variation distance of ergodic diffusion processes with initial law satisfying a given integrability condition. To this end, we give a general upper bound “à la Pinsker” enabling
Variational DistanceDependent Image Restoration
"... There is a need to restore color images that suffer from distancedependent degradation during acquisition. This occurs, for example, when imaging through scattering media. There, signal attenuation worsens with the distance of an object from the camera. A ‘naive ’ restoration may attempt to restore ..."
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Cited by 9 (6 self)
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objects (which are very noisy). We present a variational method to overcome this problem. It uses a regularization operator which is distance dependent, in addition to being edgepreserving and colorchannel coupled. Minimizing this functional results in a scheme of reconstruction
On the Jensen–Shannon Divergence and Variational Distance
"... Abstract—We study the distance measures between two probability distributions via two different distance metrics, a newmetric induced from Jensen–Shannon divergence, and the well known metric. We show that several important results and constructions in computational complexity under the metric carry ..."
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Abstract—We study the distance measures between two probability distributions via two different distance metrics, a newmetric induced from Jensen–Shannon divergence, and the well known metric. We show that several important results and constructions in computational complexity under the metric
Results 1  10
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7,649