Results 31 to 40 of about 365,701 (280)
Statistical Leakage Analysis Using Gaussian Mixture Model
IEEE Access, 2018 In the design process of advanced semiconductor devices, statistical leakage analysis has emerged as a major step due to uncertainties in the leakage current caused by the process variations. In this paper, a novel statistical leakage analysis which uses
Hyunjeong Kwon +3 more
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Geoscience Letters, 2023 Earthquake occurrence modeling of large subduction events involves significant uncertainty, stemming from the scarcity of geological data and inaccuracy of dating techniques.
Katsuichiro Goda
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Variational learning for Gaussian mixture models [PDF]
IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2006 This paper proposes a joint maximum likelihood and Bayesian methodology for estimating Gaussian mixture models. In Bayesian inference, the distributions of parameters are modeled, characterized by hyperparameters. In the case of Gaussian mixtures, the distributions of parameters are considered as Gaussian for the mean, Wishart for the covariance, and ...
Nikolaos, Nasios, Adrian G, Bors
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Regression with Gaussian Mixture ModelsApplied to Track Fitting
Instruments, 2020 This note describes the application of Gaussian mixture regression to track fitting with a Gaussian mixture model of the position errors. The mixture model is assumed to have two components with identical component means.
Rudolf Frühwirth
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Mathematics, 2023 The mixture of experts (ME) model is effective for multimodal data in statistics and machine learning. To treat non-stationary probabilistic regression, the mixture of Gaussian processes (MGP) model has been proposed, but it may not perform well in some ...
Yurong Xie, Di Wu, Zhe Qiang
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Bayesian Repulsive Gaussian Mixture Model
, 2017 We develop a general class of Bayesian repulsive Gaussian mixture models that encourage well-separated clusters, aiming at reducing potentially redundant components produced by independent priors for locations (such as the Dirichlet process).
Xie, Fangzheng, Xu, Yanxun
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Uniform convergence rates and uniform adaptive estimation in mixtures of regressions [PDF]
, 2018 In this thesis, we develop theoretical tools to examine estimators in non-parametric regression models in regard of uniform convergence rates and uniform adaptivity with respect to the smoothness of the parameter functions.
Werner, Heiko
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Responsible Gaussian Model: Matrix-Based Approximation of Gaussian Mixture Model
IEEE AccessMechanisms of deep learning are often viewed as a unclear structure and are difficult to interpret or control precisely using mathematical or engineering principles.
Wataru Obayashi +2 more
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Study on driver’s turning intention recognition hybrid model of GHMM and GGAP-RBF neural network
Advances in Mechanical Engineering, 2018 The accuracy and real time are crucial in turning intention recognition. Therefore, a hybrid model of Gaussian mixture hidden Markov and generalized growing and pruning algorithm for radial basis function neural network is constructed to recognize driver
Shu Wang, Qiang Yu, Xuan Zhao
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Supervoxel Segmentation with Voxel-Related Gaussian Mixture Model
Sensors, 2018 Extended from superpixel segmentation by adding an additional constraint on temporal consistency, supervoxel segmentation is to partition video frames into atomic segments.
Zhihua Ban, Zhong Chen, Jianguo Liu
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