Gaussian mixture density estimation
Bayesian Portfolio Selection with Gaussian Mixture Returns
... of estimation risk, nonnormality is another concern on the portfolio ...is mixture models, which, according to Geweke(2007), could date back to the work of Newcomb ...Finite Gaussian mixture ...
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A new analysis for finding the optimum power rating of low voltage distribution power electronics based on statistics and probabilities
... a mixture density, has been researched for many ...closest estimation of the parameters of an underlying distribution from a given data set [16], ...for Gaussian mixture model by using ...
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BAYESIAN CLASSIFICATION USING GAUSSIAN MIXTURE MODEL AND EM ESTIMATION: IMPLEMENTATIONS AND COMPARISONS
... the Gaussian distribution lies an assumption that the class model is truly a model of one basic ...probability density function, is multimodal, it ...single Gaussian approximation would describe a ...
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A Novel Feature Extraction Techniques for Multimodal Score Fusion Using Density Based Gaussian Mixture Model Approach
... D. Density-based score fusion: This approach is based on the likelihood ratio test and it requires explicit estimation of genuine and impostor match score ...densities. Density estimation can ...
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A Survey on Different Classifier in Speech Recognition Techniques.
... Gaussian Mixture Model is a pattern classifier which is used to build a Emotion recognition systems using extracted ...for density estimation and clustering. By using Multivariate ...
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Scale Mixture of Gaussian Modelling of Polarimetric SAR Data
... scale mixture models essentially describe the proba- bility density function giving rise to the measured complex scattering ...Parameter estimation, particularly of higher-order statistical terms, is ...
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Dirichlet Process Gaussian Mixture Models: Choice of the Base Distribution
... The Dirichlet process mixtures of Gaussians model is one of the most widely used DPM models. We have pre- sented hierarchical formulations of DPGMM with con- jugate and conditionally conjugate base distributions. The ...
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Asymptotic behavior of some Bayesian nonparametric and semi-parametric procedures
... Density estimation, which is also relevant in various applications such as cluster analysis and robust estimation, is a fundamental nonparametric inference ...to density estimation, a ...
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Semi-Parametric Mixture Models Through Log-Concave Density Estimation
... the mixture density. Based on the plot, we can see that our estimation approximates the data fairly ...be Gaussian, which makes the density estimation proposed by [21] ...
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Speaker Recognition using Gaussian Mixture Model
... IV. GAUSSIAN MIXTURE MODEL Definition of GMM specifies that it is the density function with probability parameters that are represented as a weighted sum of Gaussian component densities,[4] It ...
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Nonparametric estimation in economics: Bayesian and frequentist approaches
... nonparametric density and regression esti- mation and illustrate how these techniques can be used in economic ...side, density estimation is illustrated via finite Gaussian mixtures and a ...
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Kernel density estimation on spaces of Gaussian distributions and symmetric positive definite matrices
... Future work will focus mainly on the application of our results to dif- ferent problems in image and signal processing. Densities are useful objects in segmentation and classification. The mean shift algorithm is a ...
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A Modeling Approach for Mobile Monitoring and Supervisory Control Development based on Open-source Programming
... In Fig. 7, the container truck performs circular path. The sensor fusion technique is capable to determine the position of the vehicle at any time. The sensor fusion technique can provide container truck’s position data ...
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An Emotion Recognition System based on Right Truncated Gaussian Mixture Model
... In this paper a novel methodology for emotion recognition is using Right Truncated Gaussian Distribution is developed. The emotions were considered from the students of Gitam University with different dialects. ...
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Dirichlet Processes A gentle tutorial
... Markov chain sampling methods for Dirichlet process mixture models. The Infinite Gaussian Mixture Model[r] ...
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Gaussian Mixture Latent Vector Grammars
... present Gaussian Mixture LVeGs (GM-LVeGs), a special case of LVeGs that uses mixtures of Gaussian distributions as the weight functions of fine-grained production ...
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A Framework for Improving the Interpersonal Relationship of the Elderly with Mild Cognitive Impairment by Using Speaker Recognition and Social Network Platforms
... the Gaussian Mixture Model (GMM) and Gaussian Mixture Model-Universal Background Model (GMM-UBM) are implemented to identify the visitor via individual input ...
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Optimal Bayesian Estimation in Random Covariate Design with a Rescaled Gaussian Process Prior
... to Gaussian processes which are specified by a kernel function; specifically, kernel functions which do not admit a finite series representation, such as the squared-exponential ...independent Gaussian ...
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Estimation of a 3D motion field from a multi camera array using a multiresolution Gaussian mixture model
... more or less tight prior on the motion at l − 1, j, p(j) = i, which has the effect of a soft constraint on the motion at level l − 1 and speeds up computation, since the mean motion of the parent is a good initial guess ...
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The Modelling of Rainflow Matrices with a Mixture of Gaussian Functions
... damage estimation, a rainflow counting method is generally used for extracting the load cycles from the load histories, because the load cycles extracted in this manner corre- spond to closed hysteresis loops in ...
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