Relevant bibliographies by topics / Mixture models

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

Academic literature on the topic 'Mixture models'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Mixture models"

1

Razzaghi, Mehdi, Geoffrey J. McLachan, and Kaye E. Basford. "Mixture Models." Technometrics 33, no. 3 (1991): 365. http://dx.doi.org/10.2307/1268796.

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Razraghi, Mehdi. "Mixture Models." Technometrics 33, no. 3 (1991): 365–66. http://dx.doi.org/10.1080/00401706.1991.10484850.

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Ueda, Naonori, Ryohei Nakano, Zoubin Ghahramani, and Geoffrey E. Hinton. "SMEM Algorithm for Mixture Models." Neural Computation 12, no. 9 (2000): 2109–28. http://dx.doi.org/10.1162/089976600300015088.

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We present a split-and-merge expectation-maximization (SMEM) algorithm to overcome the local maxima problem in parameter estimation of finite mixture models. In the case of mixture models, local maxima often involve having too many components of a mixture model in one part of the space and too few in another, widely separated part of the space. To escape from such configurations, we repeatedly perform simultaneous split-and-merge operations using a new criterion for efficiently selecting the split-and-merge candidates. We apply the proposed algorithm to the training of gaussian mixtures and mi
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Achcar, Jorge A., Emílio A. Coelho-Barros, and Josmar Mazucheli. "Cure fraction models using mixture and non-mixture models." Tatra Mountains Mathematical Publications 51, no. 1 (2012): 1–9. http://dx.doi.org/10.2478/v10127-012-0001-4.

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ABSTRACT We introduce the Weibull distributions in presence of cure fraction, censored data and covariates. Two models are explored in this paper: mixture and non-mixture models. Inferences for the proposed models are obtained under the Bayesian approach, using standard MCMC (Markov Chain Monte Carlo) methods. An illustration of the proposed methodology is given considering a life- time data set.
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Le, Si Quang, Nicolas Lartillot, and Olivier Gascuel. "Phylogenetic mixture models for proteins." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1512 (2008): 3965–76. http://dx.doi.org/10.1098/rstb.2008.0180.

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Standard protein substitution models use a single amino acid replacement rate matrix that summarizes the biological, chemical and physical properties of amino acids. However, site evolution is highly heterogeneous and depends on many factors: genetic code; solvent exposure; secondary and tertiary structure; protein function; etc. These impact the substitution pattern and, in most cases, a single replacement matrix is not enough to represent all the complexity of the evolutionary processes. This paper explores in maximum-likelihood framework phylogenetic mixture models that combine several amin
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McLachlan, Geoffrey J., Sharon X. Lee, and Suren I. Rathnayake. "Finite Mixture Models." Annual Review of Statistics and Its Application 6, no. 1 (2019): 355–78. http://dx.doi.org/10.1146/annurev-statistics-031017-100325.

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The important role of finite mixture models in the statistical analysis of data is underscored by the ever-increasing rate at which articles on mixture applications appear in the statistical and general scientific literature. The aim of this article is to provide an up-to-date account of the theory and methodological developments underlying the applications of finite mixture models. Because of their flexibility, mixture models are being increasingly exploited as a convenient, semiparametric way in which to model unknown distributional shapes. This is in addition to their obvious applications w
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Shanmugam, Ramalingam. "Finite Mixture Models." Technometrics 44, no. 1 (2002): 82. http://dx.doi.org/10.1198/tech.2002.s651.

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Verbeek, J. J., N. Vlassis, and B. Kröse. "Efficient Greedy Learning of Gaussian Mixture Models." Neural Computation 15, no. 2 (2003): 469–85. http://dx.doi.org/10.1162/089976603762553004.

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This article concerns the greedy learning of gaussian mixtures. In the greedy approach, mixture components are inserted into the mixture one aftertheother.We propose a heuristic for searching for the optimal component to insert. In a randomized manner, a set of candidate new components is generated. For each of these candidates, we find the locally optimal new component and insert it into the existing mixture. The resulting algorithm resolves the sensitivity to initialization of state-of-the-art methods, like expectation maximization, and has running time linear in the number of data points an
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Nemec, James M., and Amanda F. L. Nemec. "Mixture models for studying stellar populations. II - Multivariate finite mixture models." Astronomical Journal 105 (April 1993): 1455. http://dx.doi.org/10.1086/116523.

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Focke, Walter W. "Mixture Models Based on Neural Network Averaging." Neural Computation 18, no. 1 (2006): 1–9. http://dx.doi.org/10.1162/089976606774841576.

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A modified version of the single hidden-layer perceptron architecture is proposed for modeling mixtures. A particular flexible mixture model is obtained by implementing the Box-Cox transformation as transfer function. In this case, the network response can be expressed in closed form as a weighted power mean. The quadratic Scheffé K-polynomial and the exponential Wilson equation turn out to be special forms of this general mixture model. Advantages of the proposed network architecture are that binary data sets suffice for “training” and that it is readily extended to incorporate additional mix
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Dissertations / Theses on the topic "Mixture models"

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Xiang, Sijia. "Semiparametric mixture models." Diss., Kansas State University, 2014. http://hdl.handle.net/2097/17338.

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Doctor of Philosophy<br>Department of Statistics<br>Weixin Yao<br>This dissertation consists of three parts that are related to semiparametric mixture models. In Part I, we construct the minimum profile Hellinger distance (MPHD) estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component density and the mixing proportion are unknown. Such semiparametric mixture models have been often used in biology and the sequential clustering algorithm. In Part II, we propose a new class of semiparametric
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Haider, Peter. "Prediction with Mixture Models." Phd thesis, Universität Potsdam, 2013. http://opus.kobv.de/ubp/volltexte/2014/6961/.

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Learning a model for the relationship between the attributes and the annotated labels of data examples serves two purposes. Firstly, it enables the prediction of the label for examples without annotation. Secondly, the parameters of the model can provide useful insights into the structure of the data. If the data has an inherent partitioned structure, it is natural to mirror this structure in the model. Such mixture models predict by combining the individual predictions generated by the mixture components which correspond to the partitions in the data. Often the partitioned structure is latent
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Qi, Meng. "Development in Normal Mixture and Mixture of Experts Modeling." UKnowledge, 2016. http://uknowledge.uky.edu/statistics_etds/15.

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In this dissertation, first we consider the problem of testing homogeneity and order in a contaminated normal model, when the data is correlated under some known covariance structure. To address this problem, we developed a moment based homogeneity and order test, and design weights for test statistics to increase power for homogeneity test. We applied our test to microarray about Down’s syndrome. This dissertation also studies a singular Bayesian information criterion (sBIC) for a bivariate hierarchical mixture model with varying weights, and develops a new data dependent information criterio
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Polsen, Orathai. "Nonparametric regression and mixture models." Thesis, University of Leeds, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578651.

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Nonparametric regression estimation has become popular in the last 50 years. A commonly used nonparametric method for estimating the regression curve is the kernel estimator, exemplified by the Nadaraya- Watson estimator. The first part of thesis concentrates on the important issue of how to make a good choice of smoothing parameter for the Nadaraya- Watson estimator. In this study three types of smoothing parameter selectors are investigated: cross-validation, plug-in and bootstrap. In addition, two situations are examined: the same smoothing parameter and different smoothing parameters are e
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James, S. D. "Mixture models for times series." Thesis, Swansea University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.637395.

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This thesis reviews some known results for the class of mixture models introduced by Jalali and Pemberton (1995) and presents two examples from the literature, which are based on the theory. The first has a countable number of mixture elements while the second has a finite number, <I>K</I>, and is called the Bernstein mixture model, since it involves the use of Bernstein polynomials in its construction. By including an additional parameter, λ, in the Binomial weights function, we obtain a parameterised version of the Bernstein model. The elements of the transition matrix for this model are pol
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Sandhu, Manjinder Kaur. "Optimal designs for mixture models." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1995. http://hub.hku.hk/bib/B31213583.

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Sánchez, Luis Enrique Benites. "Finite mixture of regression models." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-10052018-131627/.

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This dissertation consists of three articles, proposing extensions of finite mixtures in regression models. Here we consider a flexible class of both univariate and multivariate distributions, which allow adequate modeling of asymmetric data that have multimodality, heavy tails and outlying observations. This class has special cases such as skew-normal, skew-t, skew-slash and skew normal contaminated distributions, as well as symmetric cases. Initially, a model is proposed based on the assumption that the errors follow a finite mixture of scale mixture of skew-normal (FM-SMSN) distribution rat
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Li, Xiongya. "Robust multivariate mixture regression models." Diss., Kansas State University, 2017. http://hdl.handle.net/2097/38427.

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Doctor of Philosophy<br>Department of Statistics<br>Weixing Song<br>In this dissertation, we proposed a new robust estimation procedure for two multivariate mixture regression models and applied this novel method to functional mapping of dynamic traits. In the first part, a robust estimation procedure for the mixture of classical multivariate linear regression models is discussed by assuming that the error terms follow a multivariate Laplace distribution. An EM algorithm is developed based on the fact that the multivariate Laplace distribution is a scale mixture of the multivariate standard no
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Kunkel, Deborah Elizabeth. "Anchored Bayesian Gaussian Mixture Models." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1524134234501475.

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Evers, Ludger. "Model fitting and model selection for 'mixture of experts' models." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445776.

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Books on the topic "Mixture models"

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Lindsay, Bruce G. Mixture Models. Institute of Mathematical Statistics and American Statistical Association, 1995. http://dx.doi.org/10.1214/cbms/1462106013.

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McLachlan, Geoffrey, and David Peel. Finite Mixture Models. John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/0471721182.

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Bouguila, Nizar, and Wentao Fan, eds. Mixture Models and Applications. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-23876-6.

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Chen, Jiahua. Statistical Inference Under Mixture Models. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6141-2.

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von Davier, Matthias. Multivariate and Mixture Distribution Rasch Models. Edited by Claus H. Carstensen. Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3.

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Jepson, Allan D. Mixture models for optical flow computation. University of Toronto, Dept. of Computer Science, 1993.

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R, Hancock Gregory, and Samuelsen Karen M, eds. Advances in latent variable mixture models. Information Age Pub., 2008.

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service), SpringerLink (Online, ed. Medical Applications of Finite Mixture Models. Springer-Verlag Berlin Heidelberg, 2009.

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A, Cornell John. Experiments with mixtures: Designs, models, and the analysis of mixture data. 2nd ed. Wiley, 1990.

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Chew, Soo Hong. Mixture symmetric utility theory. Dept. of Economics, Institute for Policy Analysis, University of Toronto, 1988.

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Book chapters on the topic "Mixture models"

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Yao, Weixin, and Sijia Xiang. "Hypothesis testing and model selection for mixture models." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-6.

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Yao, Weixin, and Sijia Xiang. "Mixture models for discrete data." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-2.

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Yao, Weixin, and Sijia Xiang. "Semiparametric mixture regression models." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-10.

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Yao, Weixin, and Sijia Xiang. "Label switching for mixture models." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-5.

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Yao, Weixin, and Sijia Xiang. "Robust mixture regression models." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-7.

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Yao, Weixin, and Sijia Xiang. "Semiparametric mixture models." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-9.

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Yao, Weixin, and Sijia Xiang. "Introduction to mixture models." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-1.

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Yao, Weixin, and Sijia Xiang. "Mixture models for high-dimensional data." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-8.

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Yao, Weixin, and Sijia Xiang. "Mixture regression models." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-3.

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Yao, Weixin, and Sijia Xiang. "Bayesian mixture models." In Mixture Models. Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003038511-4.

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Conference papers on the topic "Mixture models"

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Rudić, Branislav, Markus Pichler-Scheder, and Dmitry Efrosinin. "Valid Decoding in Gaussian Mixture Models." In 2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS). IEEE, 2024. https://doi.org/10.1109/citds62610.2024.10791365.

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Li, Chuchen, Bingqing Zhao, Shanjun Tang, Xing Li, and Xin Liao. "Target point alignment with Gaussian mixture models." In Sixteenth International Conference on Signal Processing Systems (ICSPS 2024), edited by Robert Minasian and Li Chai. SPIE, 2025. https://doi.org/10.1117/12.3060665.

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Sandler, Mark. "Hierarchical mixture models." In the 13th ACM SIGKDD international conference. ACM Press, 2007. http://dx.doi.org/10.1145/1281192.1281255.

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Sak, Hasim, Cyril Allauzen, Kaisuke Nakajima, and Francoise Beaufays. "Mixture of mixture n-gram language models." In 2013 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2013. http://dx.doi.org/10.1109/asru.2013.6707701.

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Simo-Serra, Edgar, Carme Torras, and Francesc Moreno-Noguer. "Geodesic Finite Mixture Models." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.91.

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Beaufays, F., M. Weintraub, and Yochai Konig. "Discriminative mixture weight estimation for large Gaussian mixture models." In 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258). IEEE, 1999. http://dx.doi.org/10.1109/icassp.1999.758131.

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Bar-Yosef, Yossi, and Yuval Bistritz. "Discriminative simplification of mixture models." In ICASSP 2011 - 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2011. http://dx.doi.org/10.1109/icassp.2011.5946927.

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Maas, Ryan, Jeremy Hyrkas, Olivia Grace Telford, Magdalena Balazinska, Andrew Connolly, and Bill Howe. "Gaussian Mixture Models Use-Case." In the 3rd VLDB Workshop. ACM Press, 2015. http://dx.doi.org/10.1145/2803140.2803143.

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Evangelio, Ruben Heras, Michael Patzold, and Thomas Sikora. "Splitting Gaussians in Mixture Models." In 2012 9th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2012. http://dx.doi.org/10.1109/avss.2012.69.

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Yang, Zhixian, and Xiaojun Wan. "Dependency-based Mixture Language Models." In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.535.

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Reports on the topic "Mixture models"

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Lavrenko, Victor. Optimal Mixture Models in IR. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada440363.

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Mueller, Shane, Andrew Boettcher, and Michael Young. Delineating Cultural Models: Extending the Cultural Mixture Model. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada572740.

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Liu, Songqi. Mixture Models: From Latent Classes/Profiles to Latent Growth, Transitions, and Multilevel Mixture Models. Instats Inc., 2022. http://dx.doi.org/10.61700/ky72m8g8cc8x2469.

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This seminar introduces mixture modeling and explores its application in applied psychology research and beyond. Topics and worked examples include latent class analysis (LCA), latent profile analysis (LPA), LCA/LPA with covariates, multilevel LCA/LPA, growth mixture modeling (GMM), and latent transition analysis (LTA). A certificate of completion is provided at the conclusion of the seminar. For European PhD students, the seminar offers 2 ECTS Equivalent point.
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Koenker, Roger, Jiaying Gu, and Stanislav Volgushev. Testing for homogeneity in mixture models. Cemmap, 2013. http://dx.doi.org/10.1920/wp.cem.2013.0913.

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Gu, Jiaying, Stanislav Volgushev, and Roger Koenker. Testing for homogeneity in mixture models. The IFS, 2017. http://dx.doi.org/10.1920/wp.cem.2017.3917.

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Yu, Guoshen, and Guillermo Sapiro. Statistical Compressive Sensing of Gaussian Mixture Models. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada540728.

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Chen, Xiaohong, Elie Tamer, and Maria Ponomareva. Likelihood inference in some finite mixture models. Cemmap, 2013. http://dx.doi.org/10.1920/wp.cem.2013.1913.

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Steele, Russell J., Adrian E. Raftery, and Mary J. Emond. Computing Normalizing Constants for Finite Mixture Models via Incremental Mixture Importance Sampling (IMIS). Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada459853.

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Kam, Chester. Mixture Modeling for Measurement Scale Assessment. Instats Inc., 2023. http://dx.doi.org/10.61700/8ll0tq1hym0nq469.

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This seminar will introduce the use of mixture models for measurement scale assessment, covering topics such as factor analysis and careless response detection. As a set of worked examples, mixture models will be applied to multitrait-multimethod (MTMM) and bifactor models. By attending this seminar, you will learn how to understand and statistically handle different types of method or source variance using mixture models, which will improve the quality and rigor of your research. An official Instats certificate of completion is provided at the conclusion of the seminar. For European PhD stude
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Heckman, James, and Christopher Taber. Econometric Mixture Models and More General Models for Unobservables in Duration Analysis. National Bureau of Economic Research, 1994. http://dx.doi.org/10.3386/t0157.

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