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Showing 1 to 20 of 52 for “"Kullback-Leibler Divergence"”.
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Using Kullback-Leibler Divergence to Analyze the Performance of Collaborative Positioning
Geolocation accuracy is a very crucial and a life-or-death factor for rescue teams. Natural disasters or man-made disasters are just a few convincing reasons why fast and accurate position location is necessary. One way to unleash the potential of positioning systems is through the use of …
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ROBUST KULLBACK-LEIBLER DIVERGENCE AND ITS APPLICATIONS IN UNIVERSAL HYPOTHESIS TESTING AND DEVIATION DETECTION
<p>The Kullback-Leibler (KL) divergence is one of the most fundamental metrics in information theory and statistics and provides various operational interpretations in the context of mathematical communication theory and statistical hypothesis testing. The KL divergence for discrete distributions …
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Transition from integrable to chaotic domain in spectra of spin chains
… by the behavior of the "burstiness" B and the Kullback–Leibler divergence DLK(PW−D(s)|Pdata(s)), where PW−D(s) and Pdata(s) are the Wigner-Dyson and the system’s spacing distribution respectively. The B coefficient is associated to a sequence of events in the system. The Kullback–Leibler …
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Representation and transfer learning using information-theoretic approximations
… machine learning systems. Mutual information and Kullback-Leibler divergence are principled and very popular metrics to measure feature relevance and perform distribution matching, respectively. However, clean formulations of machine learning algorithms based on these information-theoretic …
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How to Rank Answers in Text Mining
… we improve this methodology by combining Kullback-Leibler divergence with CEW-DTW, since Kullback-Leibler divergence can check the difference of probability distributions in two sequences. However, CEW-DTW and KL-CEW-DTW do not care about the effect of noise and keywords from the viewpoint …
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Essay on beliefs and the macroeconomy
… endogenous distribution of observables in the Kullback-Leibler divergence. If the set of permissible beliefs contains the rational-expectations equilibria (REE), then the REE are CREE; otherwise, they are not. I show that a CREE exists, that it arises naturally as the limit of adaptive and …
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Theory and Algorithms for Penalization, Graphical Models, and Surrogate Marker Evaluation
… for maximum regularized likelihood estimators in Kullback-Leibler divergence. The result applies to a wide variety of models and estimators where the densities have a convex parametrization, and the regularization is definite and positively homogenous. Next, we introduce a general framework, the …
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A Bayesian Approach to Beamforming for Uncertain Direction -of -Arrival
… direction, where closeness is defined in the Kullback Leibler divergence sense. The rate of convergence of the beamformer is controlled by the signal-to-noise ratio (SNR) of the array. Three efficient implementation algorithms for the Bayesian beamformer are developed by exploiting the …
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A population Monte Carlo approach to estimating parametric bidirectional reflectance distribution functions through Markov random field parameter estimation
… distances between these features, including the Kullback-Leibler divergence, the Bhattacharyya distance and the Patrick-Fisher distance is used to test the classifiability of the materials, using the PMC evolved sample sets as features. In our experiments on real data, which comprises 48 material …
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The formal definition of reference priors under a general class of divergence
… priors rigorously in the contexts under Kullback-Leibler divergence. In special cases with common support and other regularity conditions, Ghosh, Mergel and Liu (2011) derived a general f-divergence criterion for prior selection. We generalize Ghosh, Mergel and Liu's (2011) results to the …
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Topics in multivariate covariance estimation and time series analysis.
… be highly computational. We propose using the Kullback-Leibler divergence as an optimization method to estimate a linear combination of class co- variance structures, which increases the accuracy of the RDA method, an limits the use of leave one out cross validation.
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Statistical inference with unreliable binary observations
… expressions for Fisher information and Kullback-Leibler divergence in terms of the design parameters and the sensor statistics. Simulations of parameter estimation, classification, and symbol detection systems show that these architectures can achieve strong performance even when the …
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Spectral discrimination of fish shoals from seafloor in the Gulf of Maine during the ocean acoustic waveguide remote sensing (OAWRS) 2006 experiment
… returns. Neyman-Pearson Hypothesis Testing, Kullback-Leibler divergence, Matched Filter and discriminating based on the shape of the spectral dependence, methods originated from Detection theory, are applied in well documented cases from Gulf of Maine during spawning season to distinguish …
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Accelerated Bayesian experimental design for chemical kinetic models
… - which reflects expected information gain (Kullback-Leibler divergence) from prior to posterior - is intractable when the likelihood is computationally intensive. Instead, polynomial chaos expansions are introduced to capture the dependence of observables on model parameters and on design …
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Three algorithms for causal learning
… of this effort, a fast algorithm to estimate the Kullback Leibler divergence of high dimensional distributions from datasets. Thirdly, I introduce a fast heuristic algorithm to optimize the number and sequence of experiments required towards complete causal discovery for different classes of …
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Bayesian Model Selection in terms of Kullback-Leibler discrepancy
… interpret. We mainly restrict attention to the Kullback-Leibler divergence, a widely applied model evaluation measurement to quantify the similarity between the proposed candidate model and the underlying true model, where the true model is only referred to a probability distribution as the best …
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Efficient hand orientation and pose estimation for uncalibrated cameras
… probabilities were then used along with a Kullback-Leibler divergence-based optimization method to estimate the marginalization weights for each regressor. A marginalization weights regressor was trained using CDF and the estimated marginalization weights. We showed the extension of our …
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An information-theoretic analysis of resampling in Sequential Monte Carlo
… to infinity. We give convergence rates of the Kullback-Leibler divergence KL ... as well as necessary and sufficient conditions for the resampled version of P to asymptotically dominate the non-resampled version from this KL divergence perspective. Versions of these results are given for both …
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Integrate template matching and statistical modeling for continuous speech recognition
… The local distances of log likelihood ratio and Kullback-Leibler divergence are proposed for dynamic time warping based template matching. In order to reduce computational complexity and storage space, we propose methods of minimum distance template selection and maximum log-likelihood template …
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Cellular diagnostic systems using hidden Markov models
… between a pair of HMMs that approximates the Kullback-Leibler divergence (KLD). Since there is no closed-form solution to calculate the KLD between the HMMs, the proposed analytical expression is very useful in classification and identification problems. A novel HMM based position location …
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