Results 11 to 20 of about 64,158 (253)
Correction: Fitting Gaussian mixture models on incomplete data [PDF]
BMC Bioinformatics, 2022 Zachary R. McCaw +2 more
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Gaussian Mixture Models Algorithm Based on Density Peaks Clustering [PDF]
Jisuanji kexue, 2021 Due to the existence of a large number of sample data which obey the Gaussian distribution,GMM (Gaussian mixture models) is used to cluster these sample data and get more accurate clustering results.In general,EM algorithm(expectation maxi-mization ...
WANG Wei-dong, XU Jin-hui, ZHANG Zhi-feng, YANG Xi-bei
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Deep Gaussian mixture models [PDF]
Statistics and Computing, 2017 Deep learning is a hierarchical inference method formed by subsequent multiple layers of learning able to more efficiently describe complex relationships. In this work, Deep Gaussian Mixture Models are introduced and discussed. A Deep Gaussian Mixture model (DGMM) is a network of multiple layers of latent variables, where, at each layer, the variables ...
Cinzia Viroli, Geoffrey J. McLachlan
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Modeling Multivariate Spray Characteristics with Gaussian Mixture Models
Energies, 2023 With the increasing demand for efficient and accurate numerical simulations of spray combustion in jet engines, the necessity for robust models to enhance the capabilities of spray models has become imperative.
Markus Wicker +5 more
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Processing tree point clouds using Gaussian Mixture Models [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013 While traditionally used for surveying and photogrammetric fields, laser scanning is increasingly being used for a wider range of more general applications.
D. Belton, S. Moncrieff, J. Chapman
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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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The R Journal, 2023 Gaussian mixture models (GMMs) are widely used for modelling stochastic problems. Indeed, a wide diversity of packages have been developed in R. However, no recent review describing the main features offered by these packages and comparing their performances has been performed.
Chassagnol, Bastien +7 more
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On the Properties of Gaussian Copula Mixture Models
Advances in Artificial Intelligence and Machine Learning, 2023 11 pages paper for theoretical properties and new algorithms for ...
Ke Wan 0001, Alain L. Kornhauser
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Model Selection for Gaussian Mixture Models [PDF]
Statistica Sinica, 2017 This paper is concerned with an important issue in finite mixture modelling, the selection of the number of mixing components. We propose a new penalized likelihood method for model selection of finite multivariate Gaussian mixture models. The proposed method is shown to be statistically consistent in determining of the number of components. A modified
Peng, H., Huang, T., Zhang, K.
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Quantum-like Gaussian mixture model [PDF]
Soft Computing, 2021 Abstract A new concept of a quantum-like mixture model is introduced. It describes the mixture distribution with the assumption that a point is generated by each Gaussian at the same time. The decision boundary of a quantum-like mixture Gaussian corresponds as well to the separation of probabilities for the switching Kalman filter. The quantum-
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