Relevant bibliographies by topics / Gamma mixture

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gamma mixture'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Gamma mixture"

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Guardone, Alberto, Piero Colonna, Emiliano Casati, and Enrico Rinaldi. "Non-classical gas dynamics of vapour mixtures." Journal of Fluid Mechanics 741 (February 13, 2014): 681–701. http://dx.doi.org/10.1017/jfm.2013.13.

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AbstractThe non-classical gas dynamics of binary mixtures of organic fluids in the vapour phase is investigated for the first time. A predictive thermodynamic model is used to compute the relevant mixture properties, including its critical point coordinates and the local value of the fundamental derivative of gas dynamics $\Gamma $. The considered model is the improved Peng–Robinson Stryjek–Vera cubic equation of state, complemented by the Wong–Sandler mixing rules. A finite thermodynamic region is found where the nonlinearity parameter $\Gamma $ is negative and therefore non-classical gas dynamics phenomena are admissible. A non-monotone dependence of $\Gamma $ on the mixture composition is observed in the case of binary mixtures of siloxane and perfluorocarbon fluids, with the minimum value of $\Gamma $ in the mixture being always larger than that of its more complex component. The observed dependence indicates that non-ideal mixing has a strong influence on the gas dynamics behaviour, either classical or non-classical, of the mixture. Numerical experiments of the supersonic expansion of a mixture flow around a sharp corner show the transition from the classical configuration, exhibiting an isentropic rarefaction fan centred at the expansion corner, to non-classical ones, including mixed expansion waves and rarefaction shock waves, if the mixture composition is changed.
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Jones, G., C. D. Lai, and J. C. W. Rayner. "A bivariate gamma mixture distribution." Communications in Statistics - Theory and Methods 29, no. 12 (January 2000): 2775–90. http://dx.doi.org/10.1080/03610920008832636.

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Block, Henry W., Naftali A. Langberg, Thomas H. Savits, and Jie Wang. "Continuous Mixtures of Exponentials and IFR Gammas Having Bathtub-Shaped Failure Rates." Journal of Applied Probability 47, no. 04 (December 2010): 899–907. http://dx.doi.org/10.1017/s0021900200007245.

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It can be seen that a mixture of an exponential distribution and a gamma distribution with increasing failure rate for the right choice of parameters can yield a distribution with a bathtub-shaped failure rate. In this paper we consider a continuous mixture of exponentials and a continuous mixture of gammas with increasing failure rates and show that the resulting mixture has a bathtub-shaped failure rate.
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Block, Henry W., Naftali A. Langberg, Thomas H. Savits, and Jie Wang. "Continuous Mixtures of Exponentials and IFR Gammas Having Bathtub-Shaped Failure Rates." Journal of Applied Probability 47, no. 4 (December 2010): 899–907. http://dx.doi.org/10.1239/jap/1294170507.

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It can be seen that a mixture of an exponential distribution and a gamma distribution with increasing failure rate for the right choice of parameters can yield a distribution with a bathtub-shaped failure rate. In this paper we consider a continuous mixture of exponentials and a continuous mixture of gammas with increasing failure rates and show that the resulting mixture has a bathtub-shaped failure rate.
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Zaman, M. R., M. K. Roy ., and N. Akhter . "Chi-square Mixture of Gamma Distribution." Journal of Applied Sciences 5, no. 9 (August 15, 2005): 1632–35. http://dx.doi.org/10.3923/jas.2005.1632.1635.

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Webb, Andrew R. "Gamma mixture models for target recognition." Pattern Recognition 33, no. 12 (December 2000): 2045–54. http://dx.doi.org/10.1016/s0031-3203(99)00195-8.

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LIU, XIN, CRISTIAN PASARICA, and YONGZHAO SHAO. "Testing Homogeneity in Gamma Mixture Models." Scandinavian Journal of Statistics 30, no. 1 (March 2003): 227–39. http://dx.doi.org/10.1111/1467-9469.00328.

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Hamed, M. S. "THE MIXTURE WEIBULL-GENERALIZED GAMMA DISTRIBUTION." Advances and Applications in Statistics 62, no. 2 (June 20, 2020): 139–71. http://dx.doi.org/10.17654/as062020139.

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Wei, Zhengyuan, Suping Li, Qiao Li, Yucan Yu, and Xiaoyang Zheng. "Gamma mixture of generalized error distribution." Communications in Statistics - Theory and Methods 49, no. 19 (May 3, 2019): 4819–33. http://dx.doi.org/10.1080/03610926.2019.1609037.

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Viziananda, S., K. Srinivasa, and P. Srinivasa. "Truncated Compound Normal with Gamma Mixture Model for Mixture Density Estimation." International Journal of Computer Applications 157, no. 3 (January 17, 2017): 6–12. http://dx.doi.org/10.5120/ijca2017912643.

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Dissertations / Theses on the topic "Gamma mixture"

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Ni, Ying. "Modeling Insurance Claim Sizes using the Mixture of Gamma & Reciprocal Gamma Distributions." Thesis, Mälardalen University, Department of Mathematics and Physics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-454.

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Schwander, Olivier. "Information-geometric methods for mixture models." Palaiseau, Ecole polytechnique, 2013. http://pastel.archives-ouvertes.fr/docs/00/93/17/22/PDF/these.pdf.

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Cette thèse présente de nouvelles méthodes pour l'apprentissage de modèles de mélanges basées sur la géométrie de l'information. Les modèles de mélanges considérés ici sont des mélanges de familles exponentielles, permettant ainsi d'englober une large part des modèles de mélanges utilisés en pratique. Grâce à la géométrie de l'information, les problèmes statistiques peuvent être traités avec des outils géométriques. Ce cadre offre de nouvelles perspectives permettant de mettre au point des algorithmes à la fois rapides et génériques. Deux contributions principales sont proposées ici. La première est une méthode de simplification d'estimateurs par noyaux. Cette simplification est effectuée à l'aide un algorithme de partitionnement, d'abord avec la divergence de Bregman puis, pour des raisons de rapidité, avec la distance de Fisher-Rao et des barycentres modèles. La seconde contribution est une généralisation de l'algorithme k-MLE permettant de traiter des mélanges où toutes les composantes ne font pas partie de la même famille: cette méthode est appliquée au cas des mélanges de Gaussiennes généralisées et des mélanges de lois Gamma et est plus rapide que les méthodes existantes. La description de ces deux méthodes est accompagnée d'une implémentation logicielle complète et leur efficacité est évaluée grâce à des applications en bio-informatique et en classification de textures
This thesis presents new methods for mixture model learning based on information geometry. We focus on mixtures of exponential families, which encompass a large number of mixtures used in practice. With information geometry, statistical problems can be studied with geometrical tools. This framework gives new perspectives allowing to design algorithms which are both fast and generic. Two main contributions are proposed here. The first one is a method for simplification of kernel density estimators. This simplification is made with clustering algorithms, first with the Bregman divergence and next, for speed reason, with the Fisher-Rao distance and model centroids. The second contribution is a generalization of the k-MLE algorithm which allows to deal with mixtures where all the components do not belong to the same family: this method is applied to mixtures of generalized Gaussians and of Gamma laws and is faster than existing methods. The description of this two algorithms comes with a complete software implementation and their efficiency is evaluated through applications in bio-informatics and texture classification
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Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter, and Bettina Grün. "Identifying mixtures of mixtures using Bayesian estimation." Taylor & Francis, 2017. http://dx.doi.org/10.1080/10618600.2016.1200472.

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The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at. In addition, this prior allows to estimate the model using standard MCMC sampling methods. In combination with a post-processing approach which resolves the label switching issue and results in an identified model, our approach allows to simultaneously (1) determine the number of clusters, (2) flexibly approximate the cluster distributions in a semi-parametric way using finite mixtures of normals and (3) identify cluster-specific parameters and classify observations. The proposed approach is illustrated in two simulation studies and on benchmark data sets.
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Borketey, Martha A. "Effects of Select Vitamin E Isoforms on the Production of Polyunsaturated Fatty Acid Metabolites in Colorectal Cancer." Digital Commons @ East Tennessee State University, 2015. https://dc.etsu.edu/etd/2480.

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Vitamin E exhibits anti-tumor activity by regulating pathways in cancer cells, potentially the lipoxygenase (LOX) pathway. We studied the effects of alpha tocopherol (AT), gamma tocopherol (GT), gamma tocotrienol (GT3), and an alpha-gamma tocopherol mixture (ATGT) on the production of the LOX metabolites 13-hydroxyoctadecaenoic acid (HODE), 15-hydroxyeicosatetraenoic acid (HETE), 12-HETE, and 5-HETE in colorectal cancer. These metabolites were examined in the HCT-116 cell line after 24 h treatment with select vitamin E isoforms and quantified by LC/MS/MS. Under physiological conditions, we find that treatment with varying vitamin E isoforms have different effects on the production of 13-HODE, 15-HETE, 12-HETE, and 5-HETE. GT increases 13-HODE and decreases 12-HETE. AT reverses the effects of GT regulation on the LOX pathway, while GT3 has no significant effect on the metabolites tested. GT shows superiority in regulating the LOX pathway as it increases 13-HODE and decreases 12-HETE for possible prevention of colorectal cancer.
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Bere, Alphonce. "Some non-standard statistical dependence problems." University of the Western Cape, 2016. http://hdl.handle.net/11394/4868.

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Philosophiae Doctor - PhD
The major result of this thesis is the development of a framework for the application of pair-mixtures of copulas to model asymmetric dependencies in bivariate data. The main motivation is the inadequacy of mixtures of bivariate Gaussian models which are commonly fitted to data. Mixtures of rotated single parameter Archimedean and Gaussian copulas are fitted to real data sets. The method of maximum likelihood is used for parameter estimation. Goodness-of-fit tests performed on the models giving the highest log-likelihood values show that the models fit the data well. We use mixtures of univariate Gaussian models and mixtures of regression models to investigate the existence of bimodality in the distribution of the widths of autocorrelation functions in a sample of 119 gamma-ray bursts. Contrary to previous findings, our results do not reveal any evidence of bimodality. We extend a study by Genest et al. (2012) of the power and significance levels of tests of copula symmetry, to two copula models which have not been considered previously. Our results confirm that for small sample sizes, these tests fail to maintain their 5% significance level and that the Cramer-von Mises-type statistics are the most powerful.
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Zens, Gregor. "Bayesian shrinkage in mixture-of-experts models: identifying robust determinants of class membership." Springer, 2019. http://dx.doi.org/10.1007/s11634-019-00353-y.

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A method for implicit variable selection in mixture-of-experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.
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Ke, Xiao. "On lower bounds of mixture L₂-discrepancy, construction of uniform design and gamma representative points with applications in estimation and simulation." HKBU Institutional Repository, 2015. https://repository.hkbu.edu.hk/etd_oa/152.

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Two topics related to the experimental design are considered in this thesis. On the one hand, the uniform experimental design (UD), a major kind of space-filling design, is widely used in applications. The majority of UD tables (UDs) with good uniformity are generated under the centralized {dollar}L_2{dollar}-discrepancy (CD) and the wrap-around {dollar}L_2{dollar}-discrepancy (WD). Recently, the mixture {dollar}L_2{dollar}-discrepancy (MD) is proposed and shown to be more reasonable than CD and WD in terms of uniformity. In first part of the thesis we review lower bounds for MD of two-level designs from a different point of view and provide a new lower bound. Following the same idea we obtain a lower bound for MD of three-level designs. Moreover, we construct UDs under the measurement of MD by the threshold accepting (TA) algorithm, and finally we attach two new UD tables with good properties derived from TA under the measurement of MD. On the other hand, the problem of selecting a specific number of representative points (RPs) to maintain as much information as a given distribution has raised attention. Previously, a method has been given to select type-II representative points (RP-II) from normal distribution. These point sets have good properties and minimize the information loss. Whereafter, following similar idea, Fu, 1985 have discussed RP-II for gamma distribution. In second part of the thesis, we improve the discussion of selecting Gamma RP-II and provide more RP-II tables with a number of parameters. Further in statistical simulation, we also evaluate the estimation performance of point sets resampled from Gamma RP-II by making comparison in different situations.
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Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter, and Bettina Grün. "Model-based clustering based on sparse finite Gaussian mixtures." Springer, 2016. http://dx.doi.org/10.1007/s11222-014-9500-2.

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In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number of non-empty components visited during MCMC sampling. Specifying a shrinkage prior, namely the normal gamma prior, on the component means leads to improved parameter estimates as well as identification of cluster-relevant variables. After estimating the mixture model using MCMC methods based on data augmentation and Gibbs sampling, an identified model is obtained by relabeling the MCMC output in the point process representation of the draws. This is performed using K-centroids cluster analysis based on the Mahalanobis distance. We evaluate our proposed strategy in a simulation setup with artificial data and by applying it to benchmark data sets. (authors' abstract)
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Janeiro, Vanderly. "Modelagem de dados contínuos censurados, inflacionados de zeros." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-20092010-090511/.

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Muitos equipamentos utilizados para quantificar substâncias, como toxinas em alimentos, freqüentemente apresentam deficiências para quantificar quantidades baixas. Em tais casos, geralmente indicam a ausência da substância quando esta existe, mas está abaixo de um valor pequeno \'ksi\' predeterminado, produzindo valores iguais a zero não necessariamente verdadeiros. Em outros casos, detectam a presença da substância, mas são incapazes de quantificá-la quando a quantidade da substância está entre \'ksai\' e um valor limiar \'tau\', conhecidos. Por outro lado, quantidades acima desse valor limiar são quantificadas de forma contínua, dando origem a uma variável aleatória contínua X cujo domínio pode ser escrito como a união dos intervalos, [ómicron, \"ksai\'), [\"ksai\', \'tau\' ] e (\'tau\', ?), sendo comum o excesso de valores iguais a zero. Neste trabalho, são propostos modelos que possibilitam discriminar a probabilidade de zeros verdadeiros, como o modelo de mistura com dois componentes, sendo um degenerado em zero e outro com distribuição contínua, sendo aqui consideradas as distribuições: exponencial, de Weibull e gama. Em seguida, para cada modelo, foram observadas suas características, propostos procedimentos para estimação de seus parâmetros e avaliados seus potenciais de ajuste por meio de métodos de simulação. Finalmente, a metodologia desenvolvida foi ilustrada por meio da modelagem de medidas de contaminação com aflatoxina B1, observadas em grãos de milho, de três subamostras de um lote de milho, analisados no Laboratório de Micotoxinas do Departamento de Agroindústria, Alimentos e Nutrição da ESALQ/USP. Como conclusões, na maioria dos casos, as simulações indicaram eficiência dos métodos propostos para as estimações dos parâmetros dos modelos, principalmente para a estimativa do parâmetro \'delta\' e do valor esperado, \'Epsilon\' (Y). A modelagem das medidas de aflatoxina, por sua vez, mostrou que os modelos propostos são adequados aos dados reais, sendo que o modelo de mistura com distribuição de Weibull, entretanto, ajustou-se melhor aos dados.
Much equipment used to quantify substances, such as toxins in foods, is unable to measure low amounts. In cases where the substance exists, but in an amount below a small fixed value \'ksi\' , the equipment usually indicates that the substance is not present, producing values equal to zero. In cases where the quantity is between \'\'ksi\' and a known threshold value \'tau\', it detects the presence of the substance but is unable to measure the amount. When the substance exists in amounts above the threshold value ?, it is measure continuously, giving rise to a continuous random variable X whose domain can be written as the union of intervals, [ómicron, \"ksai\'), [\"ksai\', \'tau\' ] and (\'tau\', ?), This random variable commonly has an excess of zero values. In this work we propose models that can detect the probability of true zero, such as the mixture model with two components, one being degenerate at zero and the other with continuous distribution, where we considered the distributions: exponential, Weibull and gamma. Then, for each model, its characteristics were observed, procedures for estimating its parameters were proposed and its potential for adjustment by simulation methods was evaluated. Finally, the methodology was illustrated by modeling measures of contamination with aflatoxin B1, detected in grains of corn from three sub-samples of a batch of corn analyzed at the laboratory of of Mycotoxins, Department of Agribusiness, Food and Nutrition ESALQ/USP. In conclusion, in the majority of cases the simulations indicated that the proposed methods are efficient in estimating the parameters of the models, in particular for estimating the parameter ? and the expected value, E(Y). The modeling of measures of aflatoxin, in turn, showed that the proposed models are appropriate for the actual data, however the mixture model with a Weibull distribution fits the data best.
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Graversen, Therese. "Statistical and computational methodology for the analysis of forensic DNA mixtures with artefacts." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:4c3bfc88-25e7-4c5b-968f-10a35f5b82b0.

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This thesis proposes and discusses a statistical model for interpreting forensic DNA mixtures. We develop methods for estimation of model parameters and assessing the uncertainty of the estimated quantities. Further, we discuss how to interpret the mixture in terms of predicting the set of contributors. We emphasise the importance of challenging any interpretation of a particular mixture, and for this purpose we develop a set of diagnostic tools that can be used in assessing the adequacy of the model to the data at hand as well as in a systematic validation of the model on experimental data. An important feature of this work is that all methodology is developed entirely within the framework of the adopted model, ensuring a transparent and consistent analysis. To overcome the challenge that lies in handling the large state space for DNA profiles, we propose a representation of a genotype that exhibits a Markov structure. Further, we develop methods for efficient and exact computation in a Bayesian network. An implementation of the model and methodology is available through the R package DNAmixtures.
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Books on the topic "Gamma mixture"

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Cheng, Russell. Finite Mixture Examples; MAPIS Details. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0018.

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Two detailed numerical examples are given in this chapter illustrating and comparing mainly the reversible jump Markov chain Monte Carlo (RJMCMC) and the maximum a posteriori/importance sampling (MAPIS) methods. The numerical examples are the well-known galaxy data set with sample size 82, and the Hidalgo stamp issues thickness data with sample size 485. A comparison is made of the estimates obtained by the RJMCMC and MAPIS methods for (i) the posterior k-distribution of the number of components, k, (ii) the predictive finite mixture distribution itself, and (iii) the posterior distributions of the component parameters and weights. The estimates obtained by MAPIS are shown to be more satisfactory and meaningful. Details are given of the practical implementation of MAPIS for five non-normal mixture models, namely: the extreme value, gamma, inverse Gaussian, lognormal, and Weibull. Mathematical details are also given of the acceptance-rejection importance sampling used in MAPIS.
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Book chapters on the topic "Gamma mixture"

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Vegas-Sánchez-Ferrero, G., M. Martín-Fernández, and J. Miguel Sanches. "A Gamma Mixture Model for IVUS Imaging." In Multi-Modality Atherosclerosis Imaging and Diagnosis, 155–71. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7425-8_13.

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McNicholas, Sharon M., Paul D. McNicholas, and Ryan P. Browne. "A Mixture of Variance-Gamma Factor Analyzers." In Contributions to Statistics, 369–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-41573-4_18.

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Ng, Shu Kay, Liming Xiang, and Kelvin Kai Wing Yau. "Mixture of Gamma Distributions for Continuous Non-Normal Data." In Mixture Modelling for Medical and Health Sciences, 49–75. Boca Raton : CRC Press, Taylor & Francis Group, 2019.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9780429195181-3.

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Chotikapanich, Duangkamon, and William E. Griffiths. "Estimating Income Distributions Using a Mixture of Gamma Densities." In Modeling Income Distributions and Lorenz Curves, 285–302. New York, NY: Springer New York, 2008. http://dx.doi.org/10.1007/978-0-387-72796-7_16.

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Schwander, Olivier, and Frank Nielsen. "Fast Learning of Gamma Mixture Models with k-MLE." In Similarity-Based Pattern Recognition, 235–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39140-8_16.

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Mallouli, Fatma, Atef Masmoudi, Afif Masmoudi, and Mohamed Abid. "Iris Localization Using Mixture of Gamma Distributions in the Segmentation Process." In Applied Mathematics in Tunisia, 215–22. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18041-0_12.

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Cai, Ling, Yiren Xu, Lei He, Yuming Zhao, and Xin Yang. "An Effective Segmentation for Noise-Based Image Verification Using Gamma Mixture Models." In Computer Vision – ACCV 2009, 21–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12297-2_3.

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Li, Yunli, and Young Jin Chun. "Stochastic Geometric Analysis of IRS-aided Wireless Networks Using Mixture Gamma Model." In Innovative Mobile and Internet Services in Ubiquitous Computing, 168–78. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79728-7_17.

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Haak, Alexander, Gonzalo Vegas-Sanchez-Ferrero, Harriët H. Mulder, Hortense A. Kirisli, Nora Baka, Coert Metz, Stefan Klein, et al. "Segmentation of 3D Transesophageal Echocardiograms by Multi-cavity Active Shape Model and Gamma Mixture Model." In Augmented Reality Environments for Medical Imaging and Computer-Assisted Interventions, 19–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40843-4_3.

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Ben Arab, Taher, Mourad Zribi, and Afif Masmoudi. "Finite Kibble’s Bivariate Gamma Mixtures for Color Image Segmentation." In Applied Mathematics in Tunisia, 245–61. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18041-0_15.

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Conference papers on the topic "Gamma mixture"

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Wu, Xin, Ling Cai, and Rongrong Ji. "Gamma Mixture Models for Outlier Removal." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451217.

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Parihar, Anil Singh. "Gaussian Mixture Model Based Adaptive Gamma Correction." In 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC). IEEE, 2017. http://dx.doi.org/10.1109/iccic.2017.8524403.

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Chauhan, P. S., Sandeep Kumar, S. K. Soni, V. K. Upaddhaya, and D. Pant. "Average Channel Capacity over Mixture Gamma Distribution." In 2020 International Conference on Electrical and Electronics Engineering (ICE3). IEEE, 2020. http://dx.doi.org/10.1109/ice348803.2020.9122966.

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Almhana, J., Z. Liu, V. Choulakian, and R. McGorman. "A Recursive Algorithm for Gamma Mixture Models." In 2006 IEEE International Conference on Communications. IEEE, 2006. http://dx.doi.org/10.1109/icc.2006.254727.

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Zou, Yan-Hui, and Heng-Chao Li. "MCMC estimation of finite generalized gamma mixture model." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6352257.

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Alghabashi, Basim, and Nizar Bouguila. "A Finite Multi-Dimensional Generalized Gamma Mixture Model." In 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData). IEEE, 2018. http://dx.doi.org/10.1109/cybermatics_2018.2018.00158.

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Phaphan, Wikanda. "Estimating Parameter for the Mixture Generalized Gamma Distribution." In the 10th International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3177457.3177492.

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Al-Osaim, Faisal R., and Nizar Bouguila. "A Finite Gamma Mixture Model-Based Discriminative Learning Frameworks." In 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA). IEEE, 2015. http://dx.doi.org/10.1109/icmla.2015.77.

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Weihermann, Jessica, Matheus Ferreira, Luís Gustavo de Castro, Francisco Ferreira, and Adalene Silva. "Unsupervised clustering of gamma-ray spectrometry data using Gaussian Mixture." In International Congress of the Brazilian Geophysical Society&Expogef. Brazilian Geophysical Society, 2019. http://dx.doi.org/10.22564/16cisbgf2019.275.

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Sun, Cheng, Yupeng Li, Pan Tang, Jianhua Zhang, and Lei Tian. "A Gamma Beta Mixture Model for Channel Multipath Components Clustering." In 2020 14th European Conference on Antennas and Propagation (EuCAP). IEEE, 2020. http://dx.doi.org/10.23919/eucap48036.2020.9135762.

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Reports on the topic "Gamma mixture"

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Meaney, Kevin Daniel. PhD Dissertation Proposal - Introduction to Dark Mix Concept: Gamma Measurements of Capsule Mixture. Office of Scientific and Technical Information (OSTI), October 2017. http://dx.doi.org/10.2172/1398900.

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Holland, J. M., L. H. Smith, E. Frome, M. J. Whitaker, L. C. Gipson, and R. J. M. Fry. Test of carcinogenicity in mouse skin: Methylenedianiline,. gamma. glycidyloxytrimethyloxysilane,. gamma. aminopropyltriethoxysilane and a mixture of m-phenylenediamine, methylenedianiline, and diglycidylether of bisphenol-A. Office of Scientific and Technical Information (OSTI), June 1987. http://dx.doi.org/10.2172/6450404.

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