Relevant bibliographies by topics / Multinomial mixture model

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Multinomial mixture model'

Author: Grafiati
Published: 24 June 2021
Last updated: 13 February 2022

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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 (January 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 (August 14, 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 (January 28, 2010): 257–70. http://dx.doi.org/10.1007/s10651-009-0131-2.

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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 admixture models, making the assumption that a document is generated from a mixture of topics. In this study, we focus on short text. Many studies have shown that the simpler assumption of a mixture model fits short text better. With mixture models, as opposed to admixture models, the generative assumption is that a document is generated from a single topic. One topic model, which makes this one-topic-per-document assumption, is the Dirichlet-multinomial mixture model. The main contributions of this work are a new Gamma-Poisson mixture model, as well as a collapsed Gibbs sampler for the model. The benefit of the collapsed Gibbs sampler derivation is that the model is able to automatically select the number of topics contained in the corpus. The results show that the Gamma-Poisson mixture model performs better than the Dirichlet-multinomial mixture model at selecting the number of topics in labelled corpora. Furthermore, the Gamma-Poisson mixture produces better topic coherence scores than the Dirichlet-multinomial mixture model, thus making it a viable option for the challenging task of topic modelling of short text.
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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 (August 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 latent variable(s), that are often of substantive interest. There are, however, settings where the local-independence axiom is not supported, and hence it is useful to consider some form of local dependence. In this paper we consider a family of models defined in terms of finite mixtures of multinomial models where the multinomials are parameterized in terms of a set of models for the univariate marginal distributions and for marginal associations. Local dependence is introduced through the models for marginal associations, and the standard latent class model obtains as a special case. Three examples are analyzed with the models to illustrate their utility in analyzing complex cross-classifications.
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Portela, J. "Clustering Discrete Data Through the Multinomial Mixture Model." Communications in Statistics - Theory and Methods 37, no. 20 (September 22, 2008): 3250–63. http://dx.doi.org/10.1080/03610920802162623.

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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 (August 2004): 463–74. http://dx.doi.org/10.1111/j.1467-9876.2004.05203.x.

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Li, Minqiang, and Liang Zhang. "Multinomial mixture model with feature selection for text clustering." Knowledge-Based Systems 21, no. 7 (October 2008): 704–8. http://dx.doi.org/10.1016/j.knosys.2008.03.025.

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Honda, Katsuhiro, Shunnya Oshio, and Akira Notsu. "Fuzzy Co-Clustering Induced by Multinomial Mixture Models." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 717–26. http://dx.doi.org/10.20965/jaciii.2015.p0717.

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A close connection between fuzzyc-means (FCM) and Gaussian mixture models (GMMs) have been discussed and several extended FCM algorithms were induced by the GMMs concept, where fuzzy partitions are proved to be more useful for revealing intrinsic cluster structures than probabilistic ones. Co-clustering is a promising technique for summarizing cooccurrence information such as document-keyword frequencies. In this paper, a fuzzy co-clustering model is induced based on the multinomial mixture models (MMMs) concept, in which the degree of fuzziness of both object and item fuzzy memberships can be properly tuned. The advantages of the dual fuzzy partition are demonstrated through several experimental results including document clustering applications.
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Lijoi, Antonio, Igor Prünster, and Tommaso Rigon. "The Pitman–Yor multinomial process for mixture modelling." Biometrika 107, no. 4 (June 5, 2020): 891–906. http://dx.doi.org/10.1093/biomet/asaa030.

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Summary Discrete nonparametric priors play a central role in a variety of Bayesian procedures, most notably when used to model latent features, such as in clustering, mixtures and curve fitting. They are effective and well-developed tools, though their infinite dimensionality is unsuited to some applications. If one restricts to a finite-dimensional simplex, very little is known beyond the traditional Dirichlet multinomial process, which is mainly motivated by conjugacy. This paper introduces an alternative based on the Pitman–Yor process, which provides greater flexibility while preserving analytical tractability. Urn schemes and posterior characterizations are obtained in closed form, leading to exact sampling methods. In addition, the proposed approach can be used to accurately approximate the infinite-dimensional Pitman–Yor process, yielding improvements over existing truncation-based approaches. An application to convex mixture regression for quantitative risk assessment illustrates the theoretical results and compares our approach with existing methods.
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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 Action Recognition. Bag-of-features representation is used to build the vocabulary of all the features and transform data that can be useful in clustering videos. Probabilistic model-based clustering, Multinomial Mixture model is used to determine the underlying clusters within the data by maximizing the expected log-likelihood and estimating the parameters of data as well as probabilities of clusters. Analysis of clusters is done to understand the genre based on dominant actions and objects. Bayesian Information Criterion(BIC) and Akaike Information Criterion(AIC) are used to determine the optimal number of clusters within the given videos. AIC/BIC scores achieved minimum scores at 32 clusters which are chosen to be the optimal number of clusters. The data is labeled with the genres and Logistic regression is performed to check the cluster performance on test data and has achieved 96% accuracy
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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 appropriés pour la résolution de nos problèmes, car il y a des variables particulières à identifier et à prendre en compte. Par conséquent, nous avons développé pour nos besoins le modèle « Logit Mixed Multinomial Adapté (LMMA) » comme outil dédié à l’analyse décisionnelle dans le choix des modes de transport des passagers. La première partie de nos travaux de recherches porte sur l’identification et la compréhension des problèmes de transports intra-cité d’origine et de destination pour le choix du mode de transport entre Jakarta et Bandung (et puis entre Bandung et Jakarta). La seconde partie concerne le processus de décision final en proposant et en analysant les résultats d’un questionnaire adressé à de nombreux utilisateurs de la liaison Jakarta-Bandung (et Bandung-Jakarta). L’analyse permet pour chaque situation d’origine et de destination, et en tenant compte des services offerts par chaque mode inter-cité, d’identifier quel est le mode le plus compétitif
An ideal city or intercity transport system is one where all the transport networks, involving in general different modes of transport, could serve together the cities connections to fulfill a passenger demand and satisfaction. Each transport network should have a logical layout (as possible with minimum discontinuities) to meet the required demands. Also in that ideal system, the different modes of transport should not only have their own good performances but also the exchange between modes should be done with harmony. The conditions as mentioned above are worldwide challenges. The present work deals with the transportation problematic between two Indonesian cities, and also with the high modal competition on the Jakarta-Bandung corridor. On that corridor, road transport is currently the main demanding mode for passengers transportation. The airlines cannot compete and discontinued their operations to this route. Nowadays, railway transport is decaying. Passengers preferences are the main variables for the final modal choice. It is necessary to know preferences due to their decisions impacts to choose one mode over the others. Those preferences are in fact not simple to express in a complex city and intercity transport system. In transportation, the Logit model is widely used as a method to explore the problematic of modal choices involving a lot of different variables. There are several Logit models already developed, such as “General Extreme Value”, “Probit”, and “Nested model”, but in this research, they are not compatible to solve our defined problems because there are some particular identified variables to be taken into account. Therefore we propose the "Adapted Mixed Multinomial Logit (AMML)" Model as a tool for analysis towards passenger's decision in modal choices. On the Jakarta-Bandung corridor, modal choices are influenced by the encountered problems in intercity transport at origin and destination. One part on this research deals with identification and understanding of the intracity transport problems of origin and destination on the choice of transport mode in Jakarta-Bandung corridor (Jakarta-Bandung and Bandung-Jakarta direction). The second part of this research deals with the final decision process by analyzing the results of questionnaires addressed to many users of the Jakarta-Bandung corridor. The five main variables of the last questionnaire are travel time, overall cost, security conditions, quality of travel information and connectivity conditions relevant to intercity transport and intracities transport conditions as well. After validation of the questionaires, this research uses the AMML model to get final decision result by comparing one mode among three intercity transport mode (train, minibus, and car) using the values of the variables. Taking into account the characteristics of each intercity mode of transportation, the analysis identifies the most competitive intercity transport mode for each situation from departure city to arrival city. Using alternative public and private transport modes policies, one could in the future modify passenger choice on intercity transport mode. Therefore, this study is relevant for improving of intracity and intercity transport systems
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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.
Research on cluster analysis continues to develop. Identifying the number of clusters and selecting a subset of relevant variables available in the data have been active areas in research on clustering methods. The approaches proposed for addressing these issues are mostly designed to deal with numerical data and cannot be directly applied for clustering categorical data. This work intends to be a contribution to handling categorical data, in this area.
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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.
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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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.

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 methodology on a subset of data from the American Community Survey (ACS). The methods can be adapted to synthesizers for nested data, as demonstrated in later chapters of the thesis.

The second contribution is a novel two-level latent class model for nested categorical data. Here, I assume that all configurations of groups and units are theoretically possible. I use a nested Dirichlet Process prior distribution for the class membership probabilities. The nested structure facilitates simultaneous modeling of variables at both group and unit levels. I illustrate the modeling by generating synthetic data and imputing missing data for a subset of data from the 2012 ACS household data. I show that the model can capture within group relationships more effectively than standard one-level latent class models.

The third contribution is a version of the nested latent class model adapted for theoretically impossible combinations, e.g. a household with two household heads or a child older than her biological father. This version assigns zero probability to those impossible groups and units. I present a proof that the Markov Chain Monte Carlo (MCMC) sampling strategy estimates the desired target distribution. I illustrate this model by generating synthetic data and imputing missing data for a subset of data from the 2011 ACS household data. The results indicate that this version can estimate the joint distribution more effectively than the previous version.


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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
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 forma a aumentar o valor da rendibilidade dos clientes já existentes, adequando assim a oferta de produtos. Nesta fase, serão obtidas as probabilidades de pertença a posteriori para classificar novos clientes nos segmentos mais adequados, permitindo contactar o cliente da melhor forma e com a melhor oferta. Numa segunda fase, será utilizado o modelo de mistura de regressões para perceber o impacto das acções de Marketing nos produtos detidos pelos clientes. Admitindo a existência de heterogeneidade das necessidades financeiras dos clientes, e colocando a hipótese de que as mesmas são explicadas com base nas acções de marketing realizadas, pretende obter-se um conjunto de estimativas de regressão para cada segmento identificado. A obtenção dessas estimativas de regressão, consoante a significância estatística, irá fornecer um maior conhecimento sobre a adequação da actual estratégia de marketing definida, e perceber a necessidade de afinação ou não da mesma consoante o segmento.
Nowadays, finite mixture models are one of the most efficient segmentation technique in the marketing field, in order to analyse structures of preferences in a given population. Based on a sample of clients of a given Financial Institution, the first step of this study applies a finite mixture model to understand the existing structures of financial needs of the clients. Based on the profiled segments, the need of developing different marketing strategies for each segment will be assessed, in order to increase the profit of the actual clients due to a correct contact strategy and offer of products. The probabilities of belonging to a certain segment will be obtained in order to alocate new clients in the most adequate segment, allowing to reach the clients with the best contact and offer of products strategy. In a second step, a regression mixture model will be applied to understand the impact of the actual marketing strategy in the portfolio of products of the clients. Assuming the existence of the heterogeneity in the financial needs of the clients and the fact that these needs can be explained by the acquisition campaigns, a set of regression models are estimated for each segment. Depending on the significance of this regression estimates, one understands the adequacy of the actual defined marketing strategy and decides if there is the need of improvement depending on the segment.
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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, 646–53. Berlin, Heidelberg: 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, 57–75. New York, NY: 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, 149–61. Singapore: 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, 438–44. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_45.

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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, 243–55. Cham: 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, 102–12. Berlin, Heidelberg: 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, 676–92. 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 data with the severity level given for each bug reports. Then, the training of classifiers is performed to predict the severity of new bug reports submitted by the user using Multinomial Naïve Bayes Classifier, Logistic Regression Classifier and Stochastic Gradient Descent Classifier. Using these methods, around 85% accuracy is obtained. More accurate predictions can be done using the authors approach.
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Kéry, Marc, and J. Andrew Royle. "Modeling Abundance Using Multinomial N-Mixture Models." In Applied Hierarchical Modeling in Ecology, 313–92. 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: ISCA, 2008. http://dx.doi.org/10.21437/interspeech.2008-258.

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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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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. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2623330.2623715.

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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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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. New York, NY, USA: 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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Kanzawa, Yuchi. "Fuzzy co-clustering induced by q-multinomial mixture models." In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2017. http://dx.doi.org/10.1109/fuzz-ieee.2017.8015398.

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