Relevant bibliographies by topics / Statistical topic models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Statistical topic models'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Statistical topic models"

1

Tang, Guoyu, Yunqing Xia, Jun Sun, Min Zhang, and Thomas Fang Zheng. "Statistical word sense aware topic models." Soft Computing 19, no. 1 (2014): 13–27. http://dx.doi.org/10.1007/s00500-014-1372-z.

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Yang, Yi, Shimei Pan, Jie Lu, Mercan Topkara, and Yangqiu Song. "The Stability and Usability of Statistical Topic Models." ACM Transactions on Interactive Intelligent Systems 6, no. 2 (2016): 1–23. http://dx.doi.org/10.1145/2954002.

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Rubin, Timothy N., America Chambers, Padhraic Smyth, and Mark Steyvers. "Statistical topic models for multi-label document classification." Machine Learning 88, no. 1-2 (2011): 157–208. http://dx.doi.org/10.1007/s10994-011-5272-5.

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Xiong, Deyi, Fandong Meng, and Qun Liu. "Topic-based term translation models for statistical machine translation." Artificial Intelligence 232 (March 2016): 54–75. http://dx.doi.org/10.1016/j.artint.2015.12.002.

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Koltcov, Sergei, and Vera Ignatenko. "Renormalization Analysis of Topic Models." Entropy 22, no. 5 (2020): 556. http://dx.doi.org/10.3390/e22050556.

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In practice, to build a machine learning model of big data, one needs to tune model parameters. The process of parameter tuning involves extremely time-consuming and computationally expensive grid search. However, the theory of statistical physics provides techniques allowing us to optimize this process. The paper shows that a function of the output of topic modeling demonstrates self-similar behavior under variation of the number of clusters. Such behavior allows using a renormalization technique. A combination of renormalization procedure with the Renyi entropy approach allows for quick sear
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Wright, Jerry H., Michael J. Carey, and Eluned S. Parris. "Topic discrimination using higher-order statistical models of spotted keywords." Computer Speech & Language 9, no. 4 (1995): 381–405. http://dx.doi.org/10.1006/csla.1995.0018.

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Et. al., K. Prashant Gokul,. "AN IMPROVED PATTERN BASED LDA TOPIC MODELING FOR BUSINESS INTELLIGENCE." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 2 (2021): 404–9. http://dx.doi.org/10.17762/itii.v9i2.363.

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Topic models give a helpful strategy to dimensionality decrease and exploratory data analysis in huge text corpora. Most ways to deal with topic model learning have been founded on a greatest likelihood objective. Proficient algorithms exist that endeavor to inexact this target, yet they have no provable certifications. As of late, algorithms have been presented that give provable limits, however these algorithms are not down to earth since they are wasteful and not hearty to infringement of model presumptions. In this work, we propose to consolidate the statistical topic modeling with pattern
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Koltcov, Sergei, Vera Ignatenko, Zeyd Boukhers, and Steffen Staab. "Analyzing the Influence of Hyper-parameters and Regularizers of Topic Modeling in Terms of Renyi Entropy." Entropy 22, no. 4 (2020): 394. http://dx.doi.org/10.3390/e22040394.

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Topic modeling is a popular technique for clustering large collections of text documents. A variety of different types of regularization is implemented in topic modeling. In this paper, we propose a novel approach for analyzing the influence of different regularization types on results of topic modeling. Based on Renyi entropy, this approach is inspired by the concepts from statistical physics, where an inferred topical structure of a collection can be considered an information statistical system residing in a non-equilibrium state. By testing our approach on four models—Probabilistic Latent S
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Serão, Nick V., Amy L. Petry, and Leticia P. Sanglard. "PSX-41 Late-Breaking Abstract: Self-reported statistical training of graduate students associated with confidence in performing statistical analyses." Journal of Animal Science 98, Supplement_4 (2020): 358. http://dx.doi.org/10.1093/jas/skaa278.627.

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Abstract Training in statistical analysis is essential to Animal Science graduate student curricula. Recently, with the increased use of high-throughput technologies in Animal Science fields, graduate students need certain statistical competencies to manage and analyze so-called “Big Data.” The objective of this study was to identify the current statistical competencies associated with graduate students’ perceived confidence to perform statistical analyses. Data from a comprehensive survey of students enrolled in Animal Science-related graduate programs (n = 354; 125 M.S. and 229 Ph.D. student
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GOTOH, YOSHIHIKO, and STEVE RENALS. "Topic-based mixture language modelling." Natural Language Engineering 5, no. 4 (1999): 355–75. http://dx.doi.org/10.1017/s1351324900002278.

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This paper describes an approach for constructing a mixture of language models based on simple statistical notions of semantics using probabilistic models developed for information retrieval. The approach encapsulates corpus-derived semantic information and is able to model varying styles of text. Using such information, the corpus texts are clustered in an unsupervised manner and a mixture of topic-specific language models is automatically created. The principal contribution of this work is to characterise the document space resulting from information retrieval techniques and to demonstrate t
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Dissertations / Theses on the topic "Statistical topic models"

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Elshamy, Wesam Samy. "Continuous-time infinite dynamic topic models." Diss., Kansas State University, 2012. http://hdl.handle.net/2097/15176.

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Doctor of Philosophy<br>Department of Computing and Information Sciences<br>William Henry Hsu<br>Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the collection that resembles the topical theme found in an article at hand. The first wave of topic models developed were able to discover the prevailing topics in a big collection of
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Putthividhya, Duangmanee (Pew). "A family of statistical topic models for text and multimedia documents." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3398161.

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Thesis (Ph. D.)--University of California, San Diego, 2010.<br>Title from first page of PDF file (viewed May 6, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 102-107).
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Forster, Jeri E. "Varying-coefficient models for longitudinal data : piecewise-continuous, flexible, mixed-effects models and methods for analyzing data with nonignorable dropout /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2006.

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Thesis (Ph.D. in Biostatistics) -- University of Colorado at Denver and Health Sciences Center, 2006.<br>Typescript. Includes bibliographical references (leaves 72-75). Free to UCD Anschutz Medical Campus. Online version available via ProQuest Digital Dissertations;
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Ayanlowo, Ayanbola Olajumoke. "Examination of novel statistical designs for phase II and phase III clinical trials." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2007. https://www.mhsl.uab.edu/dt/2008r/ayanlowo-ayanbola.pdf.

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Grey, Scott F. "How much compliance is enough? Examining the effect of different definitions of compliance on estimates of treatment efficacy in randomized controlled trials." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1363172687.

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Khan, Mohammed Salman. "A Topic Modeling approach for Code Clone Detection." UNF Digital Commons, 2019. https://digitalcommons.unf.edu/etd/874.

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In this thesis work, the potential benefits of Latent Dirichlet Allocation (LDA) as a technique for code clone detection has been described. The objective is to propose a language-independent, effective, and scalable approach for identifying similar code fragments in relatively large software systems. The main assumption is that the latent topic structure of software artifacts gives an indication of the presence of code clones. It can be hypothesized that artifacts with similar topic distributions contain duplicated code fragments and to prove this hypothesis, an experimental investigation usi
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Wagner, Brandie D. "Permutation based microarray gene selection methods with covarience adjustment applicable to complex diseases /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2007.

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Thesis (Ph.D. in Analytic Health Sciences) -- University of Colorado Denver, 2007.<br>Typescript. Includes bibliographical references (leaves 57-60). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;
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Jaros, Mark J. "A joint model for longitudinal data and competing risks /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2008.

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Thesis (Ph.D. in Biostatistics) -- University of Colorado Denver, 2008.<br>Typescript. Includes bibliographical references (leaves 117-119). Free to UCD Anschutz Medical Campus. Online version available via ProQuest Digital Dissertations;
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Heltshe, Sonya Lenore. "Quantification of length-bias in screening trials with covariate-dependent test sensitivity /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2007.

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Thesis (Ph.D. in Biostatistics, Department of Preventive Medicine and Biometrics) -- University of Colorado Denver, 2007.<br>Typescript. Includes bibliographical references (leaves 89-93). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;
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Gao, Dexiang. "Analysis of clustered longitudinal count data /." Connect to full text via ProQuest. Limited to UCD Anschutz Medical Campus, 2007.

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Thesis (Ph.D. in Analytic Health Sciences, Department of Preventive Medicine and Biometrics) -- University of Colorado Denver, 2007.<br>Typescript. Includes bibliographical references (leaves 75-77). Free to UCD affiliates. Online version available via ProQuest Digital Dissertations;
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Books on the topic "Statistical topic models"

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Statistical methods in drug combination studies. CRC Press, Taylor & Francis Group, 2015.

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Stage-wise adaptive designs. John Wiley & Sons, 2009.

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

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Bartroff, Jay. Sequential experimentation in clinical trials: Design and analysis. Springer, 2013.

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The statistical analysis of functional MRI data. Springer, 2008.

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Gould, Lawrence. Statistical methods for evaluating safety in medical product development. John Wiley & Sons Inc., 2014.

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1939-, Machin David, and Tan Say-Beng, eds. An introduction to statistics in early phase trials. John Wiley, 2010.

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Julious, Steven A. An introduction to statistics in early phase trials. John Wiley, 2010.

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Julious, Steven A. An introduction to statistics in early phase trials. John Wiley, 2010.

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Julious, Steven A. An introduction to statistics in early phase trials. John Wiley, 2010.

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Book chapters on the topic "Statistical topic models"

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Yamron, J. P., L. Gillick, P. van Mulbregt, and S. Knecht. "Statistical Models of Topical Content." In Topic Detection and Tracking. Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0933-2_6.

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Tang, Guoyu, Yunqing Xia, Jun Sun, Min Zhang, and Thomas Fang Zheng. "Topic Models Incorporating Statistical Word Senses." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54906-9_13.

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Vorontsov, Konstantin, Anna Potapenko, and Alexander Plavin. "Additive Regularization of Topic Models for Topic Selection and Sparse Factorization." In Statistical Learning and Data Sciences. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17091-6_14.

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Newman, David, Chaitanya Chemudugunta, Padhraic Smyth, and Mark Steyvers. "Analyzing Entities and Topics in News Articles Using Statistical Topic Models." In Intelligence and Security Informatics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11760146_9.

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Atkinson, Anthony C. "Some Topics in Optimum Experimental Design for Generalized Linear Models." In Statistical Modelling. Springer New York, 1995. http://dx.doi.org/10.1007/978-1-4612-0789-4_2.

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Shafiei, M. Mahdi, and Evangelos E. Milios. "A Statistical Model for Topic Segmentation and Clustering." In Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68825-9_27.

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Ruppert, David, and David S. Matteson. "Time Series Models: Further Topics." In Statistics and Data Analysis for Financial Engineering. Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2614-5_13.

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Ruppert, David. "Time Series Models: Further Topics." In Statistics and Data Analysis for Financial Engineering. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-7787-8_10.

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Wang, Xing, Deyi Xiong, Min Zhang, Yu Hong, and Jianmin Yao. "A Topic-Based Reordering Model for Statistical Machine Translation." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45924-9_37.

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Wilson, Jeffrey R., Elsa Vazquez-Arreola, and (Din) Ding-Geng Chen. "Models for." In Emerging Topics in Statistics and Biostatistics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48904-5_5.

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Conference papers on the topic "Statistical topic models"

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Newman, David, Chaitanya Chemudugunta, and Padhraic Smyth. "Statistical entity-topic models." In the 12th ACM SIGKDD international conference. ACM Press, 2006. http://dx.doi.org/10.1145/1150402.1150487.

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Reynar, Jeffrey C. "Statistical models for topic segmentation." In the 37th annual meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1999. http://dx.doi.org/10.3115/1034678.1034735.

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Chemudugunta, Chaitanya, Padhraic Smyth, and Mark Steyvers. "Combining concept hierarchies and statistical topic models." In Proceeding of the 17th ACM conference. ACM Press, 2008. http://dx.doi.org/10.1145/1458082.1458337.

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Newman, David, Kat Hagedorn, Chaitanya Chemudugunta, and Padhraic Smyth. "Subject metadata enrichment using statistical topic models." In the 2007 conference. ACM Press, 2007. http://dx.doi.org/10.1145/1255175.1255248.

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Behmardi, Behrouz, and Raviv Raich. "On provable exact low-rank recovery in topic models." In 2011 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2011. http://dx.doi.org/10.1109/ssp.2011.5967677.

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Morstatter, Fred, and Huan Liu. "A Novel Measure for Coherence in Statistical Topic Models." In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/p16-2088.

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Glenny, Vanessa, Jonathan Tuke, Nigel Bean, and Lewis Mitchell. "A framework for streamlined statistical prediction using topic models." In Proceedings of the 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-2508.

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Giesen, Joachim, Paul Kahlmeyer, Sören Laue, et al. "Method of Moments for Topic Models with Mixed Discrete and Continuous Features." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/333.

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Topic models are characterized by a latent class variable that represents the different topics. Traditionally, their observable variables are modeled as discrete variables like, for instance, in the prototypical latent Dirichlet allocation (LDA) topic model. In LDA, words in text documents are encoded by discrete count vectors with respect to some dictionary. The classical approach for learning topic models optimizes a likelihood function that is non-concave due to the presence of the latent variable. Hence, this approach mostly boils down to using search heuristics like the EM algorithm for p
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Linstead, Erik, and Pierre Baldi. "Mining the coherence of GNOME bug reports with statistical topic models." In 2009 6th IEEE International Working Conference on Mining Software Repositories (MSR). IEEE, 2009. http://dx.doi.org/10.1109/msr.2009.5069486.

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Paczkowski, Patrick, Julie Dorse, and Holly Rushmeier. "Using statistical topic models to organize and visualize large-scale architectural image databases." In ACM SIGGRAPH 2011 Posters. ACM Press, 2011. http://dx.doi.org/10.1145/2037715.2037816.

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Reports on the topic "Statistical topic models"

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Payment Systems Report - June of 2020. Banco de la República de Colombia, 2021. http://dx.doi.org/10.32468/rept-sist-pag.eng.2020.

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With its annual Payment Systems Report, Banco de la República offers a complete overview of the infrastructure of Colombia’s financial market. Each edition of the report has four objectives: 1) to publicize a consolidated account of how the figures for payment infrastructures have evolved with respect to both financial assets and goods and services; 2) to summarize the issues that are being debated internationally and are of interest to the industry that provides payment clearing and settlement services; 3) to offer the public an explanation of the ideas and concepts behind retail-value paymen
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