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Auswahl der wissenschaftlichen Literatur zum Thema „Dirichlet modeling“
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Zeitschriftenartikel zum Thema "Dirichlet modeling"
Makgai, Seitebaleng, Andriette Bekker und Mohammad Arashi. „Compositional Data Modeling through Dirichlet Innovations“. Mathematics 9, Nr. 19 (03.10.2021): 2477. http://dx.doi.org/10.3390/math9192477.
Der volle Inhalt der QuelleChauhan, Uttam, und Apurva Shah. „Topic Modeling Using Latent Dirichlet allocation“. ACM Computing Surveys 54, Nr. 7 (30.09.2022): 1–35. http://dx.doi.org/10.1145/3462478.
Der volle Inhalt der QuelleNavarro, Daniel J., Thomas L. Griffiths, Mark Steyvers und Michael D. Lee. „Modeling individual differences using Dirichlet processes“. Journal of Mathematical Psychology 50, Nr. 2 (April 2006): 101–22. http://dx.doi.org/10.1016/j.jmp.2005.11.006.
Der volle Inhalt der QuelleLingwall, Jeff W., William F. Christensen und C. Shane Reese. „Dirichlet based Bayesian multivariate receptor modeling“. Environmetrics 19, Nr. 6 (September 2008): 618–29. http://dx.doi.org/10.1002/env.902.
Der volle Inhalt der QuelleSchwarz, Carlo. „Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation“. Stata Journal: Promoting communications on statistics and Stata 18, Nr. 1 (März 2018): 101–17. http://dx.doi.org/10.1177/1536867x1801800107.
Der volle Inhalt der QuelleŞahin, Büşra, Atıf Evren, Elif Tuna, Zehra Zeynep Şahinbaşoğlu und Erhan Ustaoğlu. „Parameter Estimation of the Dirichlet Distribution Based on Entropy“. Axioms 12, Nr. 10 (05.10.2023): 947. http://dx.doi.org/10.3390/axioms12100947.
Der volle Inhalt der QuelleBouguila, N., und D. Ziou. „A Dirichlet Process Mixture of Generalized Dirichlet Distributions for Proportional Data Modeling“. IEEE Transactions on Neural Networks 21, Nr. 1 (Januar 2010): 107–22. http://dx.doi.org/10.1109/tnn.2009.2034851.
Der volle Inhalt der QuelleChristy, A., Anto Praveena und Jany Shabu. „A Hybrid Model for Topic Modeling Using Latent Dirichlet Allocation and Feature Selection Method“. Journal of Computational and Theoretical Nanoscience 16, Nr. 8 (01.08.2019): 3367–71. http://dx.doi.org/10.1166/jctn.2019.8234.
Der volle Inhalt der QuelleElliott, Lloyd T., Maria De Iorio, Stefano Favaro, Kaustubh Adhikari und Yee Whye Teh. „Modeling Population Structure Under Hierarchical Dirichlet Processes“. Bayesian Analysis 14, Nr. 2 (Juni 2019): 313–39. http://dx.doi.org/10.1214/17-ba1093.
Der volle Inhalt der QuelleLi, Yuelin, Elizabeth Schofield und Mithat Gönen. „A tutorial on Dirichlet process mixture modeling“. Journal of Mathematical Psychology 91 (August 2019): 128–44. http://dx.doi.org/10.1016/j.jmp.2019.04.004.
Der volle Inhalt der QuelleDissertationen zum Thema "Dirichlet modeling"
Heaton, Matthew J. „Temporally Correlated Dirichlet Processes in Pollution Receptor Modeling“. Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd1861.pdf.
Der volle Inhalt der QuelleHu, Zhen. „Modeling photonic crystal devices by Dirichlet-to-Neumann maps /“. access full-text access abstract and table of contents, 2009. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?phd-ma-b30082559f.pdf.
Der volle Inhalt der Quelle"Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [85]-91)
Gao, Wenyu. „Advanced Nonparametric Bayesian Functional Modeling“. Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99913.
Der volle Inhalt der QuelleDoctor of Philosophy
As we have easier access to massive data sets, functional analyses have gained more interest to analyze data providing information about curves, surfaces, or others varying over a continuum. However, such data sets often contain large heterogeneities and noise. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this dissertation focuses on the development of nonparametric Bayesian approaches for functional analyses. Our proposed methods can be applied in various applications: the epidemiological studies on aseptic meningitis with clustered binary data, the genetic diabetes data, and breast cancer racial disparities.
Monson, Rebecca Lee. „Modeling Transition Probabilities for Loan States Using a Bayesian Hierarchical Model“. Diss., CLICK HERE for online access, 2007. http://contentdm.lib.byu.edu/ETD/image/etd2179.pdf.
Der volle Inhalt der Quellelim, woobeen. „Bayesian Semiparametric Joint Modeling of Longitudinal Predictors and Discrete Outcomes“. The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1618955725276958.
Der volle Inhalt der QuelleDomingues, Rémi. „Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification“. Electronic Thesis or Diss., Sorbonne université, 2019. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2019SORUS473.pdf.
Der volle Inhalt der QuelleNovelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. While numerous novelty detection methods were designed to model continuous numerical data, tackling datasets composed of mixed-type features, such as numerical and categorical data, or temporal datasets describing discrete event sequences is a challenging task. In addition to the supported data types, the key criteria for efficient novelty detection methods are the ability to accurately dissociate novelties from nominal samples, the interpretability, the scalability and the robustness to anomalies located in the training data. In this thesis, we investigate novel ways to tackle these issues. In particular, we propose (i) a survey of state-of-the-art novelty detection methods applied to mixed-type data, including extensive scalability, memory consumption and robustness tests (ii) a survey of state-of-the-art novelty detection methods suitable for sequence data (iii) a probabilistic nonparametric novelty detection method for mixed-type data based on Dirichlet process mixtures and exponential-family distributions and (iv) an autoencoder-based novelty detection model with encoder/decoder modelled as deep Gaussian processes. The learning of this last model is made tractable and scalable through the use of random feature approximations and stochastic variational inference. The method is suitable for large-scale novelty detection problems and data with mixed-type features. The experiments indicate that the proposed model achieves competitive results with state-of-the-art novelty detection methods
Race, Jonathan Andrew. „Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model“. The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077.
Der volle Inhalt der QuelleHuo, Shuning. „Bayesian Modeling of Complex High-Dimensional Data“. Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/101037.
Der volle Inhalt der QuelleDoctor of Philosophy
With the rapid development of modern high-throughput technologies, scientists can now collect high-dimensional data in different forms, such as engineering signals, medical images, and genomics measurements. However, acquisition of such data does not automatically lead to efficient knowledge discovery. The main objective of this dissertation is to develop novel Bayesian methods to extract useful knowledge from complex high-dimensional data. It has two parts—the development of an ultra-fast functional mixed model and the modeling of data heterogeneity via Dirichlet Diffusion Trees. The first part focuses on developing approximate Bayesian methods in functional mixed models to estimate parameters and detect significant regions. Two datasets demonstrate the effectiveness of proposed method—a mass spectrometry dataset in a cancer study and a neuroimaging dataset in an Alzheimer's disease study. The second part focuses on modeling data heterogeneity via Dirichlet Diffusion Trees. The method helps uncover the underlying hierarchical tree structures and estimate systematic differences between the group of samples. We demonstrate the effectiveness of the method through the brain tumor imaging data.
Liu, Jia. „Heterogeneous Sensor Data based Online Quality Assurance for Advanced Manufacturing using Spatiotemporal Modeling“. Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78722.
Der volle Inhalt der QuellePh. D.
Bui, Quang Vu. „Pretopology and Topic Modeling for Complex Systems Analysis : Application on Document Classification and Complex Network Analysis“. Thesis, Paris Sciences et Lettres (ComUE), 2018. http://www.theses.fr/2018PSLEP034/document.
Der volle Inhalt der QuelleThe work of this thesis presents the development of algorithms for document classification on the one hand, or complex network analysis on the other hand, based on pretopology, a theory that models the concept of proximity. The first work develops a framework for document clustering by combining Topic Modeling and Pretopology. Our contribution proposes using topic distributions extracted from topic modeling treatment as input for classification methods. In this approach, we investigated two aspects: determine an appropriate distance between documents by studying the relevance of Probabilistic-Based and Vector-Based Measurements and effect groupings according to several criteria using a pseudo-distance defined from pretopology. The second work introduces a general framework for modeling Complex Networks by developing a reformulation of stochastic pretopology and proposes Pretopology Cascade Model as a general model for information diffusion. In addition, we proposed an agent-based model, Textual-ABM, to analyze complex dynamic networks associated with textual information using author-topic model and introduced Textual-Homo-IC, an independent cascade model of the resemblance, in which homophily is measured based on textual content obtained by utilizing Topic Modeling
Bücher zum Thema "Dirichlet modeling"
Liang, Percy, Michael Jordan und Dan Klein. Probabilistic grammars and hierarchical Dirichlet processes. Herausgegeben von Anthony O'Hagan und Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.27.
Der volle Inhalt der QuelleJockers, Matthew L. Theme. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037528.003.0008.
Der volle Inhalt der QuelleBuchteile zum Thema "Dirichlet modeling"
Palencia-Olivar, Miguel, Stéphane Bonnevay, Alexandre Aussem und Bruno Canitia. „Neural Embedded Dirichlet Processes for Topic Modeling“. In Modeling Decisions for Artificial Intelligence, 299–310. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85529-1_24.
Der volle Inhalt der QuelleAzamova, Nilufar A., Elena S. Alekseeva, Alexander A. Potapov und Alexander A. Rassadin. „Fractality and the Internal Dirichlet Problem“. In 13th Chaotic Modeling and Simulation International Conference, 111–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70795-8_9.
Der volle Inhalt der QuelleZamzami, Nuha, und Nizar Bouguila. „Text Modeling Using Multinomial Scaled Dirichlet Distributions“. In Lecture Notes in Computer Science, 69–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92058-0_7.
Der volle Inhalt der QuelleHa-Thuc, Viet, und Padmini Srinivasan. „A Latent Dirichlet Framework for Relevance Modeling“. In Information Retrieval Technology, 13–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04769-5_2.
Der volle Inhalt der QuelleStals, Linda, und Stephen Roberts. „Smoothing and Filling Holes with Dirichlet Boundary Conditions“. In Modeling, Simulation and Optimization of Complex Processes, 521–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-79409-7_38.
Der volle Inhalt der QuelleFlorindo, João Batista. „Dirichlet Series in Complex Network Modeling of Texture Images“. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 368–75. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13469-3_43.
Der volle Inhalt der QuelleHe, Yuan, Cheng Wang und Changjun Jiang. „Multi-perspective Hierarchical Dirichlet Process for Geographical Topic Modeling“. In Advances in Knowledge Discovery and Data Mining, 811–23. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_63.
Der volle Inhalt der QuelleOf, Günther, Thanh Xuan Phan und Olaf Steinbach. „Finite and Boundary Element Energy Approximations of Dirichlet Control Problems“. In Modeling, Simulation and Optimization of Complex Processes, 219–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25707-0_18.
Der volle Inhalt der QuelleChatterjee, Rajdeep, Chandan Mukherjee, Siddhartha Chatterjee und Biswaroop Nath. „Latent Dirichlet Allocation for Topic Modeling and Intelligent Document Classification“. In Lecture Notes in Networks and Systems, 71–83. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-4928-7_6.
Der volle Inhalt der QuelleHaritha, P., und P. Shanmugavadivu. „Optimized Latent-Dirichlet-Allocation Based Topic Modeling–An Empirical Study“. In Communications in Computer and Information Science, 412–19. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-58495-4_30.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Dirichlet modeling"
Nallamothu, Sai Karthik, Rohith Kamal Kumar Yenduri, Sai Sandeep Pippalla, Kpvm Karthik, Bhargav Sai Alapati, Sri Naga Venkata Kowshik Veldhi und Prashanthi Boyapati. „Comparative Analysis of Feature Representations for Topic Modeling with Latent Dirichlet Allocation“. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725873.
Der volle Inhalt der QuelleManuaba, Ida Bagus Kerthyayana, und Mochammad Faisal Karim. „Latent Dirichlet Allocation (LDA) Topic Modeling and Sentiment Analysis for Myanmar Coup Tweets“. In 2024 8th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 161–66. IEEE, 2024. http://dx.doi.org/10.1109/icitisee63424.2024.10730529.
Der volle Inhalt der QuelleFernandis, Rasio, Daniel Swanjaya, Risky Aswi Ramadhani, Patmi Kasih und Julian Sahertian. „Topic Modeling Using Latent Dirichlet Allocation Method Based On Child Anecdotal Record Data“. In 2024 4th International Conference of Science and Information Technology in Smart Administration (ICSINTESA), 299–304. IEEE, 2024. http://dx.doi.org/10.1109/icsintesa62455.2024.10747976.
Der volle Inhalt der QuelleGarewal, Ishmeen Kaur, Shruti Jha und Chaitanya V. Mahamuni. „Topic Modeling for Identifying Emerging Trends on Instagram Using Latent Dirichlet Allocation and Non-Negative Matrix Factorization“. In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 1103–10. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10717021.
Der volle Inhalt der QuelleNurmiati, Evy, Muhammad Qomarul Huda und Selly Mitsalina. „Sentiment Analysis and Topics Modeling on Mobile Banking Reviews of Sharia Bank in Indonesia Using Naive Bayes and Latent Dirichlet Allocation“. In 2024 12th International Conference on Cyber and IT Service Management (CITSM), 1–6. IEEE, 2024. https://doi.org/10.1109/citsm64103.2024.10775521.
Der volle Inhalt der QuelleYi-Qun Ding, Zhen Zhang und Bin Xu. „Nested Dirichlet process for collaborative mobility modeling“. In 2009 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2009. http://dx.doi.org/10.1109/icmlc.2009.5212623.
Der volle Inhalt der QuelleMadsen, Rasmus E., David Kauchak und Charles Elkan. „Modeling word burstiness using the Dirichlet distribution“. In the 22nd international conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1102351.1102420.
Der volle Inhalt der QuelleBian, Wei, und Dacheng Tao. „Dirichlet Mixture Allocation for Multiclass Document Collections Modeling“. In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.102.
Der volle Inhalt der QuelleLu, Ya Yan, Jianhua Yuan und Shaojie Li. „Modeling Photonic Crystals by Dirichlet-to-Neumann Maps“. In Integrated Photonics and Nanophotonics Research and Applications. Washington, D.C.: OSA, 2007. http://dx.doi.org/10.1364/ipnra.2007.imb1.
Der volle Inhalt der QuelleChien, Jen-Tzung. „The shared dirichlet priors for bayesian language modeling“. In ICASSP 2015 - 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2015. http://dx.doi.org/10.1109/icassp.2015.7178337.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Dirichlet modeling"
Alonso-Robisco, Andrés, José Manuel Carbó und José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Madrid: Banco de España, Februar 2023. http://dx.doi.org/10.53479/29594.
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