Relevant bibliographies by topics / Multinomial mixture model

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  1. Journal articles
  2. Dissertations / Theses
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  4. Conference papers

Academic literature on the topic 'Multinomial mixture model'

Author: Grafiati
Published: 24 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Multinomial mixture model"

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Malyutov, M. B., and D. A. Stolyarenko. "On Multisample Multinomial Mixture Model." American Journal of Mathematical and Management Sciences 21, no. 1-2 (2001): 101–7. http://dx.doi.org/10.1080/01966324.2001.10737540.

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Bashir, Shaheena, and Edward M. Carter. "Penalized multinomial mixture logit model." Computational Statistics 25, no. 1 (2009): 121–41. http://dx.doi.org/10.1007/s00180-009-0165-9.

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Holland, Mark D., and Brian R. Gray. "Multinomial mixture model with heterogeneous classification probabilities." Environmental and Ecological Statistics 18, no. 2 (2010): 257–70. http://dx.doi.org/10.1007/s10651-009-0131-2.

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Abdul Rahman, Teh Faradilla, Norshita Mat Nayan, Nurhilyana Anuar, and Aminatul Solehah Idris. "Dirichlet Multinomial Modelling Approaches in Analyzing Anxiety Therapy Messages." Journal of Information and Knowledge Management 15, no. 1 (2025): 98–108. https://doi.org/10.24191/jikm.v15i1.4541.

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Despite the effectiveness of anxiety therapy through text messages, limited research was found to analyse the topics included in the therapy session. It is also unclear of which topic modelling approaches is the best in extracting anxiety therapy topics from text messages. Thus, this study aims to compare the performance of four topic modelling methods, namely Latent Feature Di-richlet Multinomial Mixture (LFDMM), Gibbs Sampling Dirichlet Multinomi-al Mixture, Generalized Polya-urn Dirichlet Multinomial Mixture and Pois-son-based Dirichlet Multinomial Mixture Model on 28 text messages of anxi-
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Portela, J. "Clustering Discrete Data Through the Multinomial Mixture Model." Communications in Statistics - Theory and Methods 37, no. 20 (2008): 3250–63. http://dx.doi.org/10.1080/03610920802162623.

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Mazarura, Jocelyn, Alta de Waal, and Pieter de Villiers. "A Gamma-Poisson Mixture Topic Model for Short Text." Mathematical Problems in Engineering 2020 (April 29, 2020): 1–17. http://dx.doi.org/10.1155/2020/4728095.

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Most topic models are constructed under the assumption that documents follow a multinomial distribution. The Poisson distribution is an alternative distribution to describe the probability of count data. For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length. The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model. Furthermore, the few Poisson topic models in the literature are adm
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Becker, Mark P., and Ilsoon Yang. "7. Latent Class Marginal Models for Cross-Classifications of Counts." Sociological Methodology 28, no. 1 (1998): 293–325. http://dx.doi.org/10.1111/0081-1750.00050.

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The standard latent class model is a finite mixture of indirectly observed multinomial distributions, each of which is assumed to exhibit statistical independence. Latent class analysis has been applied in a wide variety of research contexts, including studies of mobility, educational attainment, agreement, and diagnostic accuracy, and as measurement error models in social research. One of the attractive features of the latent class model in these settings is that the parameters defining the individual multinomials are readily interpretable marginal probabilities, conditional on the unobserved
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Cruz-Medina, I. R., T. P. Hettmansperger, and H. Thomas. "Semiparametric mixture models and repeated measures: the multinomial cut point model." Journal of the Royal Statistical Society: Series C (Applied Statistics) 53, no. 3 (2004): 463–74. http://dx.doi.org/10.1111/j.1467-9876.2004.05203.x.

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Désir, Antoine, Vineet Goyal, and Jiawei Zhang. "Technical Note—Capacitated Assortment Optimization: Hardness and Approximation." Operations Research 70, no. 2 (2022): 893–904. http://dx.doi.org/10.1287/opre.2021.2142.

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Assortment optimization is an important problem arising in various applications. In many practical settings, the assortment is subject to a capacity constraint. In “Capacitated Assortment Optimization: Hardness and Approximation,” Désir, Goyal, and Zhang study the capacitated assortment optimization problem. The authors first show that adding a general capacity constraint makes the problem NP-hard even for the simple multinomial logit model. They also show that under the mixture of multinomial logit model, even the unconstrained problem is hard to approximate within any reasonable factor when
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Li, Minqiang, and Liang Zhang. "Multinomial mixture model with feature selection for text clustering." Knowledge-Based Systems 21, no. 7 (2008): 704–8. http://dx.doi.org/10.1016/j.knosys.2008.03.025.

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Dissertations / Theses on the topic "Multinomial mixture model"

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Frühwirth-Schnatter, Sylvia, and Rudolf Frühwirth. "Bayesian Inference in the Multinomial Logit Model." Austrian Statistical Society, 2012. http://epub.wu.ac.at/5629/1/186%2D751%2D1%2DSM.pdf.

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The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, various data-augmented Metropolis-Hastings algorithms are developed for a Bayesian inference of the MNL model.
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Vellala, Abhinay. "Genre-based Video Clustering using Deep Learning : By Extraction feature using Object Detection and Action Recognition." Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-176942.

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Social media has become an integral part of the Internet. There have been users across the world sharing content like images, texts, videos, and so on. There is a huge amount of data being generated and it has become a challenge to the social media platforms to group the content for further usage like recommending a video. Especially, grouping videos based on similarity requires extracting features. This thesis investigates potential approaches to extract features that can help in determining the similarity between videos. Features of given videos are extracted using Object Detection and Actio
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POLYMEROPOULOS, ALESSIO. "Objective Variable Selection in Multinomial Logistic Regression: a Conditional Latent Approach." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2020. http://hdl.handle.net/10281/271146.

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Mixtures of g-priors are well established in linear regression models by \cite{Liang2008} and generalized linear models by \cite{Bove2011} and \cite{Li2013} for variable selection. This approach enables us to overcome the problem of specifying the dispersion parameter by imposing a hyper-prior on it. By this way we allow for our model to "learn" about the shrinkage from the data. In this work, we implement Bayesian variable selection methods based on g-priors and their mixtures in multinomial logistic regression models. More precisely, we follow two approaches: (a) the traditional implementati
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Barus, Lita Sari. "Contribution to the intercity modal choise considering the intracity transport systems : application of an adapted mixed multinomial Logit model for the Jakarta-Bandung corridor." Thesis, Compiègne, 2015. http://www.theses.fr/2015COMP2223/document.

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Ce travail de recherche traite de la problématique des transports dans les villes d’Indonésie, Jakarta et Bandung, mais également de la grande concurrence modale du trajet Jakarta-Bandung et Bandung-Jakarta. Les préférences des passagers sont des variables très importantes à connaître en raison de leurs impacts pour choisir un mode de transport parmi d’autres. Dans les transports, le modèle Logit est largement utilisé comme une méthode pour aborder la problématique du choix de transport multimodal comportant de multiples variables, mais dans la présente recherche, ces modèles ne sont pas appro
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Silvestre, Cláudia Marisa Vasconcelos. "Clustering with discrete mixture models: An integrated approach for model selection." Doctoral thesis, 2014. http://hdl.handle.net/10071/9991.

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A investigação em analise de agrupamento (cluster analysis) continua em curso. Identificar o número de grupos, bem como seleccionar um subconjunto de variáveis relevantes a partir de dados de uma amostra constituem domínios de investigação ativa em agrupamento. Grande parte dos métodos desenvolvidos para abordar estas temáticas refere-se a dados contínuos, e não podem ser directamente aplicados ao agrupamento de dados categoriais. Este trabalho, pretende ser um contributo nesta área, abordando o agrupamento de dados categoriais.<br>Research on cluster analysis continues to develop. Identifyi
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Tomita, Y. "Multinomial mixture vector autoregressive models /." 2003. http://wwwlib.umi.com/dissertations/fullcit/3108778.

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Pan, Zhen Yu. "Large margin multinomial mixture models for document classification /." 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR51574.

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Thesis (M.Sc.)--York University, 2008. Graduate Programme in Computer Science.<br>Typescript. Includes bibliographical references (leaves 88-90). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR51574
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Hu, Jingchen. "Dirichlet Process Mixture Models for Nested Categorical Data." Diss., 2015. http://hdl.handle.net/10161/9933.

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<p>This thesis develops Bayesian latent class models for nested categorical data, e.g., people nested in households. The applications focus on generating synthetic microdata for public release and imputing missing data for household surveys, such as the 2010 U.S. Decennial Census.</p><p>The first contribution is methods for evaluating disclosure risks in fully synthetic categorical data. I quantify disclosure risks by computing Bayesian posterior probabilities that intruders can learn confidential values given the released data and assumptions about their prior knowledge. I demonstrate the met
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Calado, Claudia da Encarnação. "Modelos de mistura em CRM: uma aplicação à segmentação no sector bancário." Master's thesis, 2008. http://hdl.handle.net/10071/1755.

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JEL: C25, C52, M31<br>Actualmente, os modelos de Mistura são considerados um dos métodos de segmentação mais eficientes na área de Marketing para o estudo das estruturas de preferências. Com base numa amostra de clientes de uma Instituição Financeira, numa primeira fase, será realizada uma segmentação com base nos modelos de mistura finita, de forma a perceber as estruturas de necessidades de produtos financeiros. Com base nas perfilagens dos segmentos, será possível efectuar a avaliação da necessidade ou não de desenvolvimento de estratégias diferenciadas consoante os segmentos obtidos de fo
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Book chapters on the topic "Multinomial mixture model"

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Novovičová, Jana, and Antonín Malík. "Application of Multinomial Mixture Model to Text Classification." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_75.

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Adams, Raymond J., and Margaret L. Wu. "The Mixed-Coefficients Multinomial Logit Model: A Generalized Form of the Rasch Model." In Multivariate and Mixture Distribution Rasch Models. Springer New York, 2007. http://dx.doi.org/10.1007/978-0-387-49839-3_4.

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Hannachi, Samar, Fatma Najar, and Nizar Bouguila. "Short Text Clustering Using Generalized Dirichlet Multinomial Mixture Model." In Recent Challenges in Intelligent Information and Database Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1685-3_13.

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Najar, Fatma, and Nizar Bouguila. "Happiness Analysis with Fisher Information of Dirichlet-Multinomial Mixture Model." In Advances in Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_45.

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Ascari, Roberto, and Sonia Migliorati. "A New Regression Model for the Analysis of Microbiome Data." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_5.

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AbstractHuman microbiome data are becoming extremely common in biomedical research due to the relevant connections with different types of diseases. A widespread discrete distribution to analyze this kind of data is the Dirichletmultinomial. Despite its popularity, this distribution often fails in modeling microbiome data due to the strict parameterization imposed on its covariance matrix. The aim of this work is to propose a new distribution for analyzing microbiome data and to define a regression model based on it. The new distribution can be expressed as a structured finite mixture model wi
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Silvestre, Cláudia, Margarida G. M. S. Cardoso, and Mário Figueiredo. "An MML Embedded Approach for Estimating the Number of Clusters." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_38.

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AbstractAssuming that the data originate from a finite mixture of multinomial distributions, we study the performance of an integrated Expectation Maximization (EM) algorithm considering Minimum Message Length (MML) criterion to select the number of mixture components. The referred EM-MML approach, rather than selecting one among a set of pre-estimated candidate models (which requires running EM several times), seamlessly integrates estimation and model selection in a single algorithm. Comparisons are provided with EM combined with well-known information criteria – e.g. the Bayesian informatio
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Sason, Itay, Damian Wojtowicz, Welles Robinson, Mark D. M. Leiserson, Teresa M. Przytycka, and Roded Sharan. "A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17083-7_15.

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Shi, Rui, Tat-Seng Chua, Chin-Hui Lee, and Sheng Gao. "Bayesian Learning of Hierarchical Multinomial Mixture Models of Concepts for Automatic Image Annotation." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11788034_11.

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Pushpalatha M N and Mrunalini M. "Predicting the Severity of Open Source Bug Reports Using Unsupervised and Supervised Techniques." In Research Anthology on Usage and Development of Open Source Software. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-9158-1.ch035.

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The severity of the bug report helps for the bug triagers to prioritize the handling of bug reports for giving more importance to high critical bugs than less critical bugs, since the inexperienced developers and new users can make mistakes while assigning the severity. The manual labeling of severity is labor-intensive and time-consuming. In this article, both unsupervised and supervised learning algorithms are used to automate the prediction of bug report severity. Because the data was unlabeled, the Gaussian Mixture Model is used to group similar kinds of bug reports. The result is labeled
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Kéry, Marc, and J. Andrew Royle. "Modeling Abundance Using Multinomial N-Mixture Models." In Applied Hierarchical Modeling in Ecology. Elsevier, 2016. http://dx.doi.org/10.1016/b978-0-12-801378-6.00007-2.

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Conference papers on the topic "Multinomial mixture model"

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Pan, Zhen-Yu, and Hui Jiang. "Large margin multinomial mixture model for text categorization." In Interspeech 2008. ISCA, 2008. http://dx.doi.org/10.21437/interspeech.2008-258.

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Yin, Jianhua, and Jianyong Wang. "A dirichlet multinomial mixture model-based approach for short text clustering." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014. http://dx.doi.org/10.1145/2623330.2623715.

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Duan, Ruting, and Chunping Li. "An Adaptive Dirichlet Multinomial Mixture Model for Short Text Streaming Clustering." In 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI). IEEE, 2018. http://dx.doi.org/10.1109/wi.2018.0-108.

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Li, Ziyue, Hao Yan, Chen Zhang, Wolfgang Ketter, and Fugee Tsung. "Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering." In SIGSPATIAL '23: The 31st ACM International Conference on Advances in Geographic Information Systems. ACM, 2023. http://dx.doi.org/10.1145/3615886.3627749.

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Karlsson, Alexander, Denio Duarte, Gunnar Mathiason, and Juhee Bae. "Evaluation of the Dirichlet Process Multinomial Mixture Model for Short-Text Topic Modeling." In 2018 6th International Symposium on Computational and Business Intelligence (ISCBI). IEEE, 2018. http://dx.doi.org/10.1109/iscbi.2018.00025.

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Bregu, Ornela, and Nizar Bouguila. "Unsupervised Learning of Dirichlet Compound Negative Multinomial Mixture Model using Minorization-Maximization Approach." In 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2023. http://dx.doi.org/10.1109/ictai59109.2023.00078.

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Guo, Zhiyan, and Wentao Fan. "Image Segmentation Based on Finite IBL Mixture Model with a Dirichlet Compound Multinomial Prior." In AIPR 2020: 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition. ACM, 2020. http://dx.doi.org/10.1145/3430199.3430207.

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Mazarura, Jocelyn, and Alta de Waal. "A comparison of the performance of latent Dirichlet allocation and the Dirichlet multinomial mixture model on short text." In 2016 PRASA-RobMech International Conference. IEEE, 2016. http://dx.doi.org/10.1109/robomech.2016.7813155.

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"Unsupervised Learning of a Finite Discrete Mixture Model Based on the Multinomial Dirichlet Distribution: Application to Texture Modeling." In 4th International Workshop on Pattern Recognition in Information Systems. SciTePress - Science and and Technology Publications, 2004. http://dx.doi.org/10.5220/0002658601180127.

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Liao, Hao-yu, Karthik Boregowda, Willie Cade, and Sara Behdad. "Machine Learning to Predict Medical Devices Repair and Maintenance Needs." In ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/detc2021-71333.

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Abstract Products often experience different failure and repair needs during their lifespan. Prediction of the type of failure is crucial to the maintenance team for various reasons, such as realizing the device performance, creating standard strategies for repair, and analyzing the trade-off between cost and profit of repair. This study aims to apply machine learning tools to forecast failure types of medical devices and help the maintenance team properly decides on repair strategies based on a limited dataset. Two types of medical devices are used as the case study. The main challenge reside
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