Готові списки джерел за темами / Bivariate Gaussian mixture

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Добірка наукової літератури з теми "Bivariate Gaussian mixture"

Автор: Grafiati

Опубліковано: 8 червня 2022

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Статті в журналах з теми "Bivariate Gaussian mixture"

Frei, Oleksandr, Olav Smeland, Dominic Holland, Alexey Shadrin, Wesley Thompson, Ole Andreassen, and Anders Dale. "BIVARIATE GAUSSIAN MIXTURE MODEL FOR GWAS SUMMARY STATISTICS." European Neuropsychopharmacology 29 (2019): S898—S899. http://dx.doi.org/10.1016/j.euroneuro.2017.08.211.

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Alqahtani, Nada A., and Zakiah I. Kalantan. "Gaussian Mixture Models Based on Principal Components and Applications." Mathematical Problems in Engineering 2020 (July 31, 2020): 1–13. http://dx.doi.org/10.1155/2020/1202307.

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Data scientists use various machine learning algorithms to discover patterns in large data that can lead to actionable insights. In general, high-dimensional data are reduced by obtaining a set of principal components so as to highlight similarities and differences. In this work, we deal with the reduced data using a bivariate mixture model and learning with a bivariate Gaussian mixture model. We discuss a heuristic for detecting important components by choosing the initial values of location parameters using two different techniques: cluster means, k-means and hierarchical clustering, and default values in the “mixtools” R package. The parameters of the model are obtained via an expectation maximization algorithm. The criteria from Bayesian point are evaluated for both techniques, demonstrating that both techniques are efficient with respect to computation capacity. The effectiveness of the discussed techniques is demonstrated through a simulation study and using real data sets from different fields.

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Wójcik, R., Peter A. Troch, H. Stricker, P. Torfs, E. Wood, H. Su, and Z. Su. "Mixtures of Gaussians for Uncertainty Description in Bivariate Latent Heat Flux Proxies." Journal of Hydrometeorology 7, no. 3 (June 1, 2006): 330–45. http://dx.doi.org/10.1175/jhm491.1.

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Abstract This paper proposes a new probabilistic approach for describing uncertainty in the ensembles of latent heat flux proxies. The proxies are obtained from hourly Bowen ratio and satellite-derived measurements, respectively, at several locations in the southern Great Plains region in the United States. The novelty of the presented approach is that the proxies are not considered separately, but as bivariate samples from an underlying probability density function. To describe the latter, the use of Gaussian mixture density models—a class of nonparametric, data-adaptive probability density functions—is proposed. In this way any subjective assumptions (e.g., Gaussianity) on the form of bivariate latent heat flux ensembles are avoided. This makes the estimated mixtures potentially useful in nonlinear interpolation and nonlinear probabilistic data assimilation of noisy latent heat flux measurements. The results in this study show that both of these applications are feasible through regionalization of estimated mixture densities. The regionalization scheme investigated here utilizes land cover and vegetation fraction as discriminatory variables.

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Alotaibi, Refah, Mervat Khalifa, Ehab M. Almetwally, Indranil Ghosh, and Rezk H. "Classical and Bayesian Inference of a Mixture of Bivariate Exponentiated Exponential Model." Journal of Mathematics 2021 (October 16, 2021): 1–20. http://dx.doi.org/10.1155/2021/5200979.

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Exponentiated exponential (EE) model has been used effectively in reliability, engineering, biomedical, social sciences, and other applications. In this study, we introduce a new bivariate mixture EE model with two parameters assuming two cases, independent and dependent random variables. We develop a bivariate mixture starting from two EE models assuming two cases, two independent and two dependent EE models. We study some useful statistical properties of this distribution, such as marginals and conditional distributions and product moments and conditional moments. In addition, we study a dependent case, a new mixture of the bivariate model based on EE distribution marginal with two parameters and with a bivariate Gaussian copula. Different methods of estimation for the model parameters are used both under the classical and under the Bayesian paradigm. Some simulation studies are presented to verify the performance of the estimation methods of the proposed model. To illustrate the flexibility of the proposed model, a real dataset is reanalyzed.

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Al-Mutairi, Dhaifalla K. "Properties of an inverse Gaussian mixture of bivariate exponential distribution and its generalization." Statistics & Probability Letters 33, no. 4 (May 1997): 359–65. http://dx.doi.org/10.1016/s0167-7152(96)00184-8.

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Rabbani, Hossein, Milan Sonka, and Michael D. Abramoff. "Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain." International Journal of Biomedical Imaging 2013 (2013): 1–23. http://dx.doi.org/10.1155/2013/417491.

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In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

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Yi, Sang-ri, Ziqi Wang, and Junho Song. "Bivariate Gaussian mixture-based equivalent linearization method for stochastic seismic analysis of nonlinear structures." Earthquake Engineering & Structural Dynamics 47, no. 3 (November 7, 2017): 678–96. http://dx.doi.org/10.1002/eqe.2985.

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Lalpawimawha, Ralte, and Arvind Pandey. "A Mixture Shared Inverse Gaussian Frailty Model under Modified Weibull Baseline Distribution." Austrian Journal of Statistics 49, no. 2 (February 20, 2020): 31–42. http://dx.doi.org/10.17713/ajs.v49i2.914.

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Frailty models are used in the survival analysis to account for the unobserved heterogeneityin individual risks to disease and death. To analyze the bivariate data on relatedsurvival times (e.g. matched pairs experiments, twin or family data), the shared frailtymodels were suggested. In this manuscript, we propose a new mixture shared inverse Gaussian frailty model based on modified Weibull as baseline distribution. The Bayesian approach of Markov Chain Monte Carlo technique is employed to estimate the parameters involved in the models. In addition, a simulation study is performed to compare the true values of the parameters with the estimated values. A comparison with the existing model was done by using Bayesian comparison techniques. A better model for infectious disease data related to kidney infection is suggested.

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G.V.S., Rajkumar, Srinivasa Rao K., and Srinivasa Rao P. "Image Segmentation and Retrievals based on Finite Doubly Truncated Bivariate Gaussian Mixture Model and KMeans." International Journal of Computer Applications 25, no. 4 (July 31, 2011): 5–13. http://dx.doi.org/10.5120/3022-4087.

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Gournelos, T., V. Kotinas, and S. Poulos. "Fitting a Gaussian mixture model to bivariate distributions of monthly river flows and suspended sediments." Journal of Hydrology 590 (November 2020): 125166. http://dx.doi.org/10.1016/j.jhydrol.2020.125166.

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Дисертації з теми "Bivariate Gaussian mixture"

Berard, Caroline. "Modèles à variables latentes pour des données issues de tiling arrays : Applications aux expériences de ChIP-chip et de transcriptome." Thesis, Paris, AgroParisTech, 2011. http://www.theses.fr/2011AGPT0067.

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Les puces tiling arrays sont des puces à haute densité permettant l'exploration des génomes à grande échelle. Elles sont impliquées dans l'étude de l'expression des gènes et de la détection de nouveaux transcrits grâce aux expériences de transcriptome, ainsi que dans l'étude des mécanismes de régulation de l'expression des gènes grâce aux expériences de ChIP-chip. Dans l'objectif d'analyser des données de ChIP-chip et de transcriptome, nous proposons une modélisation fondée sur les modèles à variables latentes, en particulier les modèles de Markov cachés, qui sont des méthodes usuelles de classification non-supervisée. Les caractéristiques biologiques du signal issu des puces tiling arrays telles que la dépendance spatiale des observations le long du génome et l'annotation structurale sont intégrées dans la modélisation. D'autre part, les modèles sont adaptés en fonction de la question biologique et une modélisation est proposée pour chaque type d'expériences. Nous proposons un mélange de régressions pour la comparaison de deux échantillons dont l'un peut être considéré comme un échantillon de référence (ChIP-chip), ainsi qu'un modèle gaussien bidimensionnel avec des contraintes sur la matrice de variance lorsque les deux échantillons jouent des rôles symétriques (transcriptome). Enfin, une modélisation semi-paramétrique autorisant des distributions plus flexibles pour la loi d'émission est envisagée. Dans un objectif de classification, nous proposons un contrôle de faux-positifs dans le cas d'une classification à deux groupes et pour des observations indépendantes. Puis, nous nous intéressons à la classification d'un ensemble d'observations constituant une région d'intérêt, telle que les gènes. Les différents modèles sont illustrés sur des jeux de données réelles de ChIP-chip et de transcriptome issus d'une puce NimbleGen couvrant le génome entier d'Arabidopsis thaliana
Tiling arrays make possible a large scale exploration of the genome with high resolution. Biological questions usually addressed are either the gene expression or the detection of transcribed regions which can be investigated via transcriptomic experiments, and also the regulation of gene expression thanks to ChIP-chip experiments. In order to analyse ChIP-chip and transcriptomic data, we propose latent variable models, especially Hidden Markov Models, which are part of unsupervised classification methods. The biological features of the tiling arrays signal, such as the spatial dependence between observations along the genome and structural annotation are integrated in the model. Moreover, the models are adapted to the biological question at hand and a model is proposed for each type of experiment. We propose a mixture of regressions for the comparison of two samples, when one sample can be considered as a reference sample (ChIP-chip), and a two-dimensional Gaussian model with constraints on the variance parameter when the two samples play symmetrical roles (transcriptome). Finally, a semi-parametric modeling is considered, allowing more flexible emission distributions. With the objective of classification, we propose a false-positive control in the case of a two-cluster classification and for independent observations. Then, we focus on the classification of a set of observations forming a region of interest such as a gene. The different models are illustrated on real ChIP-chip and transcriptomic datasets coming from a NimbleGen tiling array covering the entire genome of Arabidopsis thaliana

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Kuo, Wei-Chien, and 郭緯謙. "MAP-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/bx24kf.

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碩士
國立交通大學
電子研究所
106
In this thesis, we aim to develop a machine learning method to calibrate the thermal sensor and to avoid the interference from the environment for higher accuracy level in human body temperature measurement. The sensing part are two resistive sensing circuits, one circuit is for detecting human body temperature, while the other is for sensing the die temperature. This sensing circuits can translate the differential resistance from the sensing- ends into digital code. By using those two thermal outputs, we train the two-dimensional multivariate Gaussian model for each temperature. Then estimate the result from the probability method to obtain the higher accuracy. After calibration, we can avoid the interference and get the results more accurately in human body temperature measurement. The monitor platform includes a sensor chip that is fabricated in the process of UMC 0.18µm CMOS-MEMS technology and the embedded system(ARM V2M-MPS2) to achieve a real-time measurement and displays current information we need. The measurement results show that the method is effective in approving the accuracy to 0.1 degree Celsius.

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Частини книг з теми "Bivariate Gaussian mixture"

Rajkumar, G. V. S., K. Srinivasa Rao, and P. Srinivasa Rao. "Colour Image Segmentation with Integrated Left Truncated Bivariate Gaussian Mixture Model and Hierarchical Clustering." In Advances in Intelligent Systems and Computing, 163–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35314-7_19.

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Тези доповідей конференцій з теми "Bivariate Gaussian mixture"

Kuo, Wei-Chien, Li-Wei Liu, Yen-Chin Liao, and Hsie-Chia Chang. "ML-based Thermal Sensor Calibration by Bivariate Gaussian Mixture Model Estimation." In 2019 32nd IEEE International System-on-Chip Conference (SOCC). IEEE, 2019. http://dx.doi.org/10.1109/socc46988.2019.1570561880.

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Yi, Sang-Ri, Ziqi Wang, and Junho Song. "Stochastic Seismic Analysis by Bivariate Gaussian Mixture based Equivalent Linearization Method." In Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management. Singapore: Research Publishing Services, 2018. http://dx.doi.org/10.3850/978-981-11-2726-7_cdse06.

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Liu, Jie, Xiahai Zhuang, Jing Liu, Shaoting Zhang, Guotai Wang, Lianming Wu, Jianrong Xu, and Lixu Gu. "Myocardium segmentation combining T2 and DE MRI using Multi-Component Bivariate Gaussian mixture model." In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6868013.

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Rabbani, H., M. Vafadoost, I. Selesnick, and S. Gazor. "Image Denoising Based on A Mixture of Bivariate Gaussian Models in Complex Wavelet Domain." In 2006 3rd IEEE/EMBS International Summer School on Medical Devices and Biosensors. IEEE, 2006. http://dx.doi.org/10.1109/issmdbs.2006.360121.

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