Relevant bibliographies by topics / Dirichlet modeling

Contents

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

Academic literature on the topic 'Dirichlet modeling'

Author: Grafiati
Published: 21 December 2024

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Journal articles on the topic "Dirichlet modeling"

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Makgai, Seitebaleng, Andriette Bekker, and Mohammad Arashi. "Compositional Data Modeling through Dirichlet Innovations." Mathematics 9, no. 19 (October 3, 2021): 2477. http://dx.doi.org/10.3390/math9192477.

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The Dirichlet distribution is a well-known candidate in modeling compositional data sets. However, in the presence of outliers, the Dirichlet distribution fails to model such data sets, making other model extensions necessary. In this paper, the Kummer–Dirichlet distribution and the gamma distribution are coupled, using the beta-generating technique. This development results in the proposal of the Kummer–Dirichlet gamma distribution, which presents greater flexibility in modeling compositional data sets. Some general properties, such as the probability density functions and the moments are presented for this new candidate. The method of maximum likelihood is applied in the estimation of the parameters. The usefulness of this model is demonstrated through the application of synthetic and real data sets, where outliers are present.
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Chauhan, Uttam, and Apurva Shah. "Topic Modeling Using Latent Dirichlet allocation." ACM Computing Surveys 54, no. 7 (September 30, 2022): 1–35. http://dx.doi.org/10.1145/3462478.

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We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace. In this work, we study the background and advancement of topic modeling techniques. We first introduce the preliminaries of the topic modeling techniques and review its extensions and variations, such as topic modeling over various domains, hierarchical topic modeling, word embedded topic models, and topic models in multilingual perspectives. Besides, the research work for topic modeling in a distributed environment, topic visualization approaches also have been explored. We also covered the implementation and evaluation techniques for topic models in brief. Comparison matrices have been shown over the experimental results of the various categories of topic modeling. Diverse technical challenges and future directions have been discussed.
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Navarro, Daniel J., Thomas L. Griffiths, Mark Steyvers, and Michael D. Lee. "Modeling individual differences using Dirichlet processes." Journal of Mathematical Psychology 50, no. 2 (April 2006): 101–22. http://dx.doi.org/10.1016/j.jmp.2005.11.006.

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Lingwall, Jeff W., William F. Christensen, and C. Shane Reese. "Dirichlet based Bayesian multivariate receptor modeling." Environmetrics 19, no. 6 (September 2008): 618–29. http://dx.doi.org/10.1002/env.902.

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Schwarz, Carlo. "Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation." Stata Journal: Promoting communications on statistics and Stata 18, no. 1 (March 2018): 101–17. http://dx.doi.org/10.1177/1536867x1801800107.

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In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications.
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Şahin, Büşra, Atıf Evren, Elif Tuna, Zehra Zeynep Şahinbaşoğlu, and Erhan Ustaoğlu. "Parameter Estimation of the Dirichlet Distribution Based on Entropy." Axioms 12, no. 10 (October 5, 2023): 947. http://dx.doi.org/10.3390/axioms12100947.

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The Dirichlet distribution as a multivariate generalization of the beta distribution is especially important for modeling categorical distributions. Hence, its applications vary within a wide range from modeling cell probabilities of contingency tables to modeling income inequalities. Thus, it is commonly used as the conjugate prior of the multinomial distribution in Bayesian statistics. In this study, the parameters of a bivariate Dirichlet distribution are estimated by entropy formalism. As an alternative to maximum likelihood and the method of moments, two methods based on the principle of maximum entropy are used, namely the ordinary entropy method and the parameter space expansion method. It is shown that in estimating the parameters of the bivariate Dirichlet distribution, the ordinary entropy method and the parameter space expansion method give the same results as the method of maximum likelihood. Thus, we emphasize that these two methods can be used alternatively in modeling bivariate and multinomial Dirichlet distributions.
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Bouguila, N., and D. Ziou. "A Dirichlet Process Mixture of Generalized Dirichlet Distributions for Proportional Data Modeling." IEEE Transactions on Neural Networks 21, no. 1 (January 2010): 107–22. http://dx.doi.org/10.1109/tnn.2009.2034851.

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Christy, A., Anto Praveena, and Jany Shabu. "A Hybrid Model for Topic Modeling Using Latent Dirichlet Allocation and Feature Selection Method." Journal of Computational and Theoretical Nanoscience 16, no. 8 (August 1, 2019): 3367–71. http://dx.doi.org/10.1166/jctn.2019.8234.

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In this information age, Knowledge discovery and pattern matching plays a significant role. Topic Modeling, an area of Text mining is used detecting hidden patterns in a document collection. Topic Modeling and Document Clustering are two important key terms which are similar in concepts and functionality. In this paper, topic modeling is carried out using Latent Dirichlet Allocation-Brute Force Method (LDA-BF), Latent Dirichlet Allocation-Back Tracking (LDA-BT), Latent Semantic Indexing (LSI) method and Nonnegative Matrix Factorization (NMF) method. A hybrid model is proposed which uses Latent Dirichlet Allocation (LDA) for extracting feature terms and Feature Selection (FS) method for feature reduction. The efficiency of document clustering depends upon the selection of good features. Topic modeling is performed by enriching the good features obtained through feature selection method. The proposed hybrid model produces improved accuracy than K-Means clustering method.
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Elliott, Lloyd T., Maria De Iorio, Stefano Favaro, Kaustubh Adhikari, and Yee Whye Teh. "Modeling Population Structure Under Hierarchical Dirichlet Processes." Bayesian Analysis 14, no. 2 (June 2019): 313–39. http://dx.doi.org/10.1214/17-ba1093.

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Li, Yuelin, Elizabeth Schofield, and 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.

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Dissertations / Theses on the topic "Dirichlet modeling"

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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.

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Hu, 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.

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Thesis (Ph.D.)--City University of Hong Kong, 2009.
"Submitted to Department of Mathematics in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves [85]-91)
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Gao, Wenyu. "Advanced Nonparametric Bayesian Functional Modeling." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99913.

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Functional analyses have gained more interest as we have easier access to massive data sets. However, such data sets often contain large heterogeneities, noise, and dimensionalities. 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, or developed from a more generic one by changing the prior distributions. Hence, this dissertation focuses on the development of Bayesian approaches for functional analyses due to their flexibilities. A nonparametric Bayesian approach, such as the Dirichlet process mixture (DPM) model, has a nonparametric distribution as the prior. This approach provides flexibility and reduces assumptions, especially for functional clustering, because the DPM model has an automatic clustering property, so the number of clusters does not need to be specified in advance. Furthermore, a weighted Dirichlet process mixture (WDPM) model allows for more heterogeneities from the data by assuming more than one unknown prior distribution. It also gathers more information from the data by introducing a weight function that assigns different candidate priors, such that the less similar observations are more separated. Thus, the WDPM model will improve the clustering and model estimation results. In this dissertation, we used an advanced nonparametric Bayesian approach to study functional variable selection and functional clustering methods. We proposed 1) a stochastic search functional selection method with application to 1-M matched case-crossover studies for aseptic meningitis, to examine the time-varying unknown relationship and find out important covariates affecting disease contractions; 2) a functional clustering method via the WDPM model, with application to three pathways related to genetic diabetes data, to identify essential genes distinguishing between normal and disease groups; and 3) a combined functional clustering, with the WDPM model, and variable selection approach with application to high-frequency spectral data, to select wavelengths associated with breast cancer racial disparities.
Doctor 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.
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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.

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lim, 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.

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Domingues, 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.

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La détection de nouveauté est le problème non supervisé d’identification d’anomalies dans des données de test qui diffèrent de manière significative des données d’apprentissage. La représentation de données temporelles ou de données de types mixtes, telles des données numériques et catégorielles, est une tâche complexe. Outre le type de données supporté, l'efficacité des méthodes de détection de nouveauté repose également sur la capacité à dissocier avec précision les anomalies des échantillons nominaux, l'interprétabilité, la scalabilité et la robustesse aux anomalies présentes dans les données d'entraînement. Dans cette thèse, nous explorons de nouvelles façons de répondre à ces contraintes. Plus spécifiquement, nous proposons (i) une étude de l'état de l'art des méthodes de détection de nouveauté, appliquée aux données de types mixtes, et évaluant la scalabilité, la consommation mémoire et la robustesse des méthodes (ii) une étude des méthodes de détection de nouveauté adaptées aux séquences d'évènements (iii) une méthode de détection de nouveauté probabiliste et non paramétrique pour les données de types mixtes basée sur des mélanges de processus de Dirichlet et des distributions de famille exponentielle et (iv) un modèle de détection de nouveauté basé sur un autoencodeur dans lequel l'encodeur et le décodeur sont modélisés par des processus Gaussiens profonds. L’apprentissage de ce modèle est effectué par extension aléatoire des dimensions et par inférence stochastique variationnelle. Cette méthode est adaptée aux dimensions de types mixtes et aux larges volumes de données
Novelty 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
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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.

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Huo, Shuning. "Bayesian Modeling of Complex High-Dimensional Data." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/101037.

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With the rapid development of modern high-throughput technologies, scientists can now collect high-dimensional complex data in different forms, such as medical images, genomics measurements. However, acquisition of more data does not automatically lead to better knowledge discovery. One needs efficient and reliable analytical tools to extract useful information from complex datasets. The main objective of this dissertation is to develop innovative Bayesian methodologies to enable effective and efficient knowledge discovery from complex high-dimensional data. It contains two parts—the development of computationally efficient functional mixed models and the modeling of data heterogeneity via Dirichlet Diffusion Tree. The first part focuses on tackling the computational bottleneck in Bayesian functional mixed models. We propose a computational framework called variational functional mixed model (VFMM). This new method facilitates efficient data compression and high-performance computing in basis space. We also propose a new multiple testing procedure in basis space, which can be used to detect significant local regions. The effectiveness of the proposed model is demonstrated through two datasets, a mass spectrometry dataset in a cancer study and a neuroimaging dataset in an Alzheimer's disease study. The second part is about modeling data heterogeneity by using Dirichlet Diffusion Trees. We propose a Bayesian latent tree model that incorporates covariates of subjects to characterize the heterogeneity and uncover the latent tree structure underlying data. This innovative model may reveal the hierarchical evolution process through branch structures and estimate systematic differences between groups of samples. We demonstrate the effectiveness of the model through the simulation study and a brain tumor real data.
Doctor 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.
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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.

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Online quality assurance is crucial for elevating product quality and boosting process productivity in advanced manufacturing. However, the inherent complexity of advanced manufacturing, including nonlinear process dynamics, multiple process attributes, and low signal/noise ratio, poses severe challenges for both maintaining stable process operations and establishing efficacious online quality assurance schemes. To address these challenges, four different advanced manufacturing processes, namely, fused filament fabrication (FFF), binder jetting, chemical mechanical planarization (CMP), and the slicing process in wafer production, are investigated in this dissertation for applications of online quality assurance, with utilization of various sensors, such as thermocouples, infrared temperature sensors, accelerometers, etc. The overarching goal of this dissertation is to develop innovative integrated methodologies tailored for these individual manufacturing processes but addressing their common challenges to achieve satisfying performance in online quality assurance based on heterogeneous sensor data. Specifically, three new methodologies are created and validated using actual sensor data, namely, (1) Real-time process monitoring methods using Dirichlet process (DP) mixture model for timely detection of process changes and identification of different process states for FFF and CMP. The proposed methodology is capable of tackling non-Gaussian data from heterogeneous sensors in these advanced manufacturing processes for successful online quality assurance. (2) Spatial Dirichlet process (SDP) for modeling complex multimodal wafer thickness profiles and exploring their clustering effects. The SDP-based statistical control scheme can effectively detect out-of-control wafers and achieve wafer thickness quality assurance for the slicing process with high accuracy. (3) Augmented spatiotemporal log Gaussian Cox process (AST-LGCP) quantifying the spatiotemporal evolution of porosity in binder jetting parts, capable of predicting high-risk areas on consecutive layers. This work fills the long-standing research gap of lacking rigorous layer-wise porosity quantification for parts made by additive manufacturing (AM), and provides the basis for facilitating corrective actions for product quality improvements in a prognostic way. These developed methodologies surmount some common challenges of advanced manufacturing which paralyze traditional methods in online quality assurance, and embody key components for implementing effective online quality assurance with various sensor data. There is a promising potential to extend them to other manufacturing processes in the future.
Ph. D.
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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.

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Les travaux de cette thèse présentent le développement d'algorithmes de classification de documents d'une part, ou d'analyse de réseaux complexes d'autre part, en s'appuyant sur la prétopologie, une théorie qui modélise le concept de proximité. Le premier travail développe un cadre pour la classification de documents en combinant une approche de topicmodeling et la prétopologie. Notre contribution propose d'utiliser des distributions de sujets extraites à partir d'un traitement topic-modeling comme entrées pour des méthodes de classification. Dans cette approche, nous avons étudié deux aspects : déterminer une distance adaptée entre documents en étudiant la pertinence des mesures probabilistes et des mesures vectorielles, et effet réaliser des regroupements selon plusieurs critères en utilisant une pseudo-distance définie à partir de la prétopologie. Le deuxième travail introduit un cadre général de modélisation des Réseaux Complexes en développant une reformulation de la prétopologie stochastique, il propose également un modèle prétopologique de cascade d'informations comme modèle général de diffusion. De plus, nous avons proposé un modèle agent, Textual-ABM, pour analyser des réseaux complexes dynamiques associés à des informations textuelles en utilisant un modèle auteur-sujet et nous avons introduit le Textual-Homo-IC, un modèle de cascade indépendant de la ressemblance, dans lequel l'homophilie est fondée sur du contenu textuel obtenu par un topic-model
The 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
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Books on the topic "Dirichlet modeling"

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Liang, Percy, Michael Jordan, and Dan Klein. Probabilistic grammars and hierarchical Dirichlet processes. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.27.

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This article focuses on the use of probabilistic context-free grammars (PCFGs) in natural language processing involving a large-scale natural language parsing task. It describes detailed, highly-structured Bayesian modelling in which model dimension and complexity responds naturally to observed data. The framework, termed hierarchical Dirichlet process probabilistic context-free grammar (HDP-PCFG), involves structured hierarchical Dirichlet process modelling and customized model fitting via variational methods to address the problem of syntactic parsing and the underlying problems of grammar induction and grammar refinement. The central object of study is the parse tree, which can be used to describe a substantial amount of the syntactic structure and relational semantics of natural language sentences. The article first provides an overview of the formal probabilistic specification of the HDP-PCFG, algorithms for posterior inference under the HDP-PCFG, and experiments on grammar learning run on the Wall Street Journal portion of the Penn Treebank.
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Jockers, Matthew L. Theme. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037528.003.0008.

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This chapter demonstrates how big data and computation can be used to identify and track recurrent themes as the products of external influence. It first considers the limitations of the Google Ngram Viewer as a tool for tracing thematic trends over time before turning to Douglas Biber's Corpus Linguistics: Investigating Language Structure and Use, a primer on various factors complicating word-focused text analysis and the subsequent conclusions one might draw regarding word meanings. It then discusses the results of the author's application of latent Dirichlet allocation (LDA) to a corpus of 3,346 nineteenth-century novels using the open-source MALLET (MAchine Learning for LanguagE Toolkit), a software package for topic modeling. It also explains the different types of analyses performed by the author, including text segmentation, word chunking, and author nationality, gender and time-themes relationship analyses. The thematic data from the LDA model reveal the degree to which author nationality, author gender, and date of publication could be predicted by the thematic signals expressed in the nineteenth-century novels corpus.
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Book chapters on the topic "Dirichlet modeling"

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Palencia-Olivar, Miguel, Stéphane Bonnevay, Alexandre Aussem, and 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.

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Azamova, Nilufar A., Elena S. Alekseeva, Alexander A. Potapov, and 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.

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Zamzami, Nuha, and 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.

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Ha-Thuc, Viet, and 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.

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Stals, Linda, and 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.

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Florindo, 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.

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He, Yuan, Cheng Wang, and 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.

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Of, Günther, Thanh Xuan Phan, and 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.

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Chatterjee, Rajdeep, Chandan Mukherjee, Siddhartha Chatterjee, and 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.

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Haritha, P., and 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.

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Conference papers on the topic "Dirichlet modeling"

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Nallamothu, Sai Karthik, Rohith Kamal Kumar Yenduri, Sai Sandeep Pippalla, Kpvm Karthik, Bhargav Sai Alapati, Sri Naga Venkata Kowshik Veldhi, and 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.

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Manuaba, Ida Bagus Kerthyayana, and 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.

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Fernandis, Rasio, Daniel Swanjaya, Risky Aswi Ramadhani, Patmi Kasih, and 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.

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Garewal, Ishmeen Kaur, Shruti Jha, and 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.

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Nurmiati, Evy, Muhammad Qomarul Huda, and 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.

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Yi-Qun Ding, Zhen Zhang, and 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.

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Madsen, Rasmus E., David Kauchak, and 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.

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Bian, Wei, and 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.

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Lu, Ya Yan, Jianhua Yuan, and 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.

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Chien, 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.

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Reports on the topic "Dirichlet modeling"

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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Madrid: Banco de España, February 2023. http://dx.doi.org/10.53479/29594.

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Abstract:
Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area.
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