[PDF] Top 20 A Kullback Leibler Divergence for Bayesian Model Diagnostics
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A Kullback Leibler Divergence for Bayesian Model Diagnostics
... rem 2. However, our results would need further refine- ment when the normality assumptions given in (3) and (4) are not suitable (see Example 3.7). As shown in Section 4, model comparison in medical research may ... See full document
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Kullback Leibler divergence based wind turbine fault feature extraction
... data-driven model-based approaches. As for the model-based approaches, it is not compulsory to consider the inner physical characteristic and process of the targeted system, compared with the mechanistic ... See full document
6
On the Importance of the Kullback Leibler Divergence Term in Variational Autoencoders for Text Generation
... With powerful autoregressive decoders, such as LSTMs, the internal decoder’s cells are likely to suffice for representing the sentence, leading to a sub-optimal solution where the decoder ignores the inferred latent code ... See full document
10
Gaussian approximations for probability measures on Rd*
... a Bayesian procedure converges to a Gaussian distribution centered at any consistent estimator (for instance, the maximum likelihood estimator (MLE)) in the limit of large data (or, relatedly, small noise ...in ... See full document
31
Derivation of the PHD and CPHD Filters Based on Direct Kullback-Leibler Divergence Minimization
... Inference in multi-target systems has a host of applications in many different disciplines such as radar/sonar tracking, navigation, air traffic control, computer vision and robotics [1]–[5]. The random finite set (RFS) ... See full document
10
Strong Consistency of the Prototype Based Clustering in Probabilistic Space
... Based model (Hinneburg and Keim, ...clustering model in probabilistic space with Kullback Leibler (KL) divergence (Dhillon et ... See full document
11
The Entropic Linkage between Equity and Bond Market Dynamics
... Abstract: An alternative derivation of the yield curve based on entropy or the loss of information as it is communicated through time is introduced. Given this focus on entropy growth in communication the Shannon entropy ... See full document
13
Some statistical inferences on the upper record of Lomax distribution
... (MLE), Kullback-Leibler Divergence of the Survival function (DLS) and ...new model is fitted on the real data and some of the comparative criteria are calculated to confirm the superiority of ... See full document
12
Nonlocal total variation based on symmetric Kullback-Leibler divergence for the ultrasound image despeckling
... speckle model of the ultrasound images is not subjected to the Gaussian ...a Bayesian estimator to the weighting function and Rayleigh-distributed speckle pattern through the intro- duction of ... See full document
12
Probabilistic sensitivity analysis of offshore wind turbines using a transformed Kullback Leibler divergence
... the divergence to analyse the sensitivity emerged first in Park and Ahn (1994), where the KL discriminator is used to assess and compare probabilistic sensitivity ...the divergence as a cross-entropy ... See full document
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Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions
... each model from the trade-off between bias and ...Contrastive Divergence (CD) as the inferencing technique for the ...the Kullback–Leibler (KL) divergence into bias and variance ... See full document
8
Gaussian Kullback-Leibler Approximate Inference
... Gaussian Kullback-Leibler (G-KL) variational approximate inference techniques for Bayesian generalised linear models and various ...and model applicability issues of G-KL versus other Gaussian ... See full document
48
Discrimination between Gamma and Log-Normal Distributions by Ratio of Minimized Kullback-Leibler Divergence
... However, the KLD (based test statistic) is considered as a ruler to measure the similarity between the two hypotheses/distributions. We define our test statistic as the natural logarithm of two ratios of KLDs. The idea ... See full document
11
Kullback-Leibler Divergence for the Normal-Gamma Distribution
... In neuroimaging, especially functional magnetic resonance imaging (fMRI), GLMs as given by equation (24) are applied to time series of neural data y (Friston et al., 1995). The design matrix X is specified by the ... See full document
12
Sensitivity to Prior Specification in Bayesian Identification of Autoregressive Time Series Models
... the Kullback-Leibler (KL) divergence (Kullback and Leibler, 1951) to measure the distance between the posteriors of the AR model order, resulting from different types of priors, ... See full document
15
Zipf–Mandelbrot law, f divergences and the Jensen type interpolating inequalities
... In recent investigations of relation (2) and its numerous consequences, it appeared as a fruitful field for many significant results. We accentuate those which deal with this relation in view of superadditivity and ... See full document
20
Association of Jensen’s inequality for s convex function with Csiszár divergence
... theory. Divergence measures have vast applications in a variety of fields such as economics, biology, signal processing, pattern recognition, computational learning, color image segmentation, magnetic resonance ... See full document
14
Stochastic Wasserstein Autoencoder for Probabilistic Sentence Generation
... However, training VAEs in NLP is more diffi- cult than the image domain (Kingma and Welling, 2014). The VAE training involves a reconstruction loss and a Kullback–Leibler (KL) divergence be- tween ... See full document
9
Population Genetics Inference for Longitudinally-Sampled Mutants Under Strong Selection
... When the population size was small (N = 100), the two methods that approximated the transition distribution with a Gaussian density performed less well compared to the standard diffusion approximation when selection was ... See full document
46
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- ... See full document
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