kullback-leibler divergence
Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization
... Abstract—In this paper, we provide novel derivations of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters without using probability generating functionals or functional derivatives. We show ...
10
On the Importance of the Kullback Leibler Divergence Term in Variational Autoencoders for Text Generation
... 2 Kullback-Leibler Divergence in VAE We take the encoder-decoder of VAEs as the sender-receiver in a communication network. Given an input message x, a sender generates a compressed encoding of x ...
10
Discrimination between Gamma and Log-Normal Distributions by Ratio of Minimized Kullback-Leibler Divergence
... of Kullback-Leibler Divergence (information or distance) based test statistic and its usage in practice, but its application on discrimination of two known and overlapping distributions has less ...
11
A non symmetric divergence and kullback leibler divergence measure
... Information divergence measures and their bounds are well known in the literature of Information -symmetric information divergence symmetric divergence measure in terms of Kullback- ...
6
Kullback Leibler divergence based wind turbine fault feature extraction
... The paper addresses the problem of fault feature extraction and selection of monitoring variables by employing Kullback-Leibler divergence (KLD) and kernel support vector machine (KSVM). In this ...
6
Kullback-Leibler Divergence for the Normal-Gamma Distribution
... the Kullback-Leibler divergence of two normal-gamma distributions using earlier results on the KL divergence for multivariate normal and univariate gamma ...KL divergence for the NG ...
12
Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback Leibler divergence
... the Kullback-Leibler divergence measure in order to make it more suitable for application in a probabilistic global sensitivity analysis of reliability appli- ...the Kullback-Leibler ...
24
Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling
... Methods: In this paper, a nonlocal total variation (NLTV) method for ultrasonic speckle reduction is proposed. A spatiogram similarity measurement is introduced for the similarity calculation between image patches. It is ...
12
Bounds for Kullback-Leibler divergence
... The purpose of this paper is to present new bounds for relative entropy D ( p || q ) of two probability distributions and then to apply them to simple entropy and mutual information1. Th[r] ...
6
A Kullback Leibler Divergence for Bayesian Model Diagnostics
... [3]; Kullback and Leibler [4]; Lindley [5]; Bernardo [6]; Schwarz ...The Kullback- Leibler distance (KLD) is perhaps the most commonly used information criterion for assessing model discrep- ...
13
Kullback Leibler approximation for probability measures on infinite dimensional spaces
... Abstract. In a variety of applications it is important to extract information from a probability measure μ on an infinite dimensional space. Examples include the Bayesian approach to inverse problems and (possibly ...
33
Approximations of Shannon Mutual Information for Discrete Variables with Applications to Neural Population Coding
... We have derived several asymptotic bounds and effective approximations of mutual information for discrete variables and established several relationships among different approximations. Our final approximation formulas ...
21
On preferred point geometry in statistics
... Abstract. A brief synopsis of progress in differential geometry in statistics is followed by a note of some points of tension in the developing relationship between these disciplines. The preferred point nature of much ...
18
Some statistical inferences on the upper record of Lomax distribution
... In this paper, some inferential properties of the upper record of the Lomax distribution are inves- tigated. The upper record of the Lomax distribution parameters are estimated by using methods, Moment (MME), Maximum ...
12
Zipf–Mandelbrot law, f divergences and the Jensen type interpolating inequalities
... the Kullback–Leibler divergence, Hellinger distance, Bhattacharyya distance (coefficient), χ 2 -divergence, total variation dis- tance and triangular ...
20
Adverse Factors for the Technology Modernization: Local Public Management
... Statistical entropy is a measure of dispersion or spread of a random variable. Especially when the random variable is nominal, classical measures of dispersion like standard deviation can not be computed. In such cases, ...
16
Strong Consistency of the Prototype Based Clustering in Probabilistic Space
... Consistency is a key property of all statistical procedures analyzing randomly sampled data. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms (von Luxburg et al., ...
11
Population Genetics Inference for Longitudinally-Sampled Mutants Under Strong Selection
... the Kullback–Leibler divergence of each approximate distribution from the exact distribution, demonstrates the superior perfor- mance of our method compared to the standard and Gaussian diffusion ...
46
Lower Bounds and Aggregation in Density Estimation
... We prove lower bounds for aggregation of model selection type of M density estimators for the Kullback-Leibler divergence (KL), the Hellinger’s distance and the L 1 -distance.. The lower[r] ...
11
Statistical inferences for correlated observations : prediction and estimation
... KL divergence as the main tool to compare different predictive distributions, and derived some explicit results for one-way random effects ...KL divergence is quite general, and can be used for other ...
118