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Journal articles on the topic 'Multinomial dirichlet classification model'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

de Waal, Daan, Roelof Coetzer, and Sean van der Merwe. "Classification of multiple Dirichlet observations under a Multinomial Model." Chemometrics and Intelligent Laboratory Systems 150 (January 2016): 51–57. http://dx.doi.org/10.1016/j.chemolab.2015.10.012.

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

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Despite the effectiveness of anxiety therapy through text messages, limited research was found to analyse the topics included in the therapy session. It is also unclear of which topic modelling approaches is the best in extracting anxiety therapy topics from text messages. Thus, this study aims to compare the performance of four topic modelling methods, namely Latent Feature Di-richlet Multinomial Mixture (LFDMM), Gibbs Sampling Dirichlet Multinomi-al Mixture, Generalized Polya-urn Dirichlet Multinomial Mixture and Pois-son-based Dirichlet Multinomial Mixture Model on 28 text messages of anxi-

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Kudrekar, Sheelavathy Veerabhadrappa, and Udaya Rani Vinayakamurthy. "Classification of malware using multinomial linked latent modular double q learning." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 577. http://dx.doi.org/10.11591/ijeecs.v28.i1.pp577-586.

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In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detec

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Kudrekar, Sheelavathy Veerabhadrappa, and Udaya Rani Vinayaka Murthy. "Classification of malware using multinomial linked latent modular double q learning." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 1 (2022): 577–86. https://doi.org/10.11591/ijeecs.v28.i1.pp577-586.

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In recent times, malware has progressed by utilizing distinct advanced machine learning techniques for detection. However, the model becomes complicated and the singular value decomposition and depth-based malware detectors failed to detect the malware significantly with minimum time and overhead. This paper proposes a multinomial linked latent dirichlet and modular double q learning (MLLD-MDQL) to efficiently detect malware based on the network behavior patterns. First, multinomial linked latent dirichlet network behavior extraction (ML-LDNBE) is applied to the input network for anomaly detec

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Frimane, Âzeddine, Mohammed Aggour, Badr Ouhammou, and Lahoucine Bahmad. "A Dirichlet-multinomial mixture model-based approach for daily solar radiation classification." Solar Energy 171 (September 2018): 31–39. http://dx.doi.org/10.1016/j.solener.2018.06.059.

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

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Most topic models are constructed under the assumption that documents follow a multinomial distribution. The Poisson distribution is an alternative distribution to describe the probability of count data. For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length. The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model. Furthermore, the few Poisson topic models in the literature are adm

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Ogura, Hiroshi, Hiromi Amano, and Masato Kondo. "Gamma-Poisson Distribution Model for Text Categorization." ISRN Artificial Intelligence 2013 (April 4, 2013): 1–17. http://dx.doi.org/10.1155/2013/829630.

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We introduce a new model for describing word frequency distributions in documents for automatic text classification tasks. In the model, the gamma-Poisson probability distribution is used to achieve better text modeling. The framework of the modeling and its application to text categorization are demonstrated with practical techniques for parameter estimation and vector normalization. To investigate the efficiency of our model, text categorization experiments were performed on 20 Newsgroups, Reuters-21578, Industry Sector, and TechTC-100 datasets. The results show that the model allows perform

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Koochemeshkian, Pantea, Eddy Ihou Koffi, and Nizar Bouguila. "Hidden Variable Models in Text Classification and Sentiment Analysis." Electronics 13, no. 10 (2024): 1859. http://dx.doi.org/10.3390/electronics13101859.

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In this paper, we are proposing extensions to the multinomial principal component analysis (MPCA) framework, which is a Dirichlet (Dir)-based model widely used in text document analysis. The MPCA is a discrete analogue to the standard PCA (it operates on continuous data using Gaussian distributions). With the extensive use of count data in modeling nowadays, the current limitations of the Dir prior (independent assumption within its components and very restricted covariance structure) tend to prevent efficient processing. As a result, we are proposing some alternatives with flexible priors suc

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Wong, Tzu-Tsung. "Generalized Dirichlet priors for Naïve Bayesian classifiers with multinomial models in document classification." Data Mining and Knowledge Discovery 28, no. 1 (2012): 123–44. http://dx.doi.org/10.1007/s10618-012-0296-4.

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10

Maku, T. O., M. O. Adenomon, M. U. Adehi, and S. E. Chaku. "Reliability of supervised topic models over unsupervised topic models for the prediction task." Science World Journal 19, no. 4 (2025): 959–67. https://doi.org/10.4314/swj.v19i4.8.

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The study investigated the depth of machine learning's capacity to perform prediction tasks. The study used textual data, specifically the daily actions of cryptocurrency (Bitcoin) dealers, which were found in news articles. The data was employed merely because it produced crowd knowledge of trade from News articles that affected the market price trend. For the goal of making predictions, 4073 pre-processed, scraped news articles from CNBC's market section website were analysed using the Latent Dirichlet Allocation (LDA) model and its variation, the Supervised Latent Dirichlet Allocation Model

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11

Li, Ximing, Jiaojiao Zhang, and Jihong Ouyang. "Dirichlet Multinomial Mixture with Variational Manifold Regularization: Topic Modeling over Short Texts." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 7884–91. http://dx.doi.org/10.1609/aaai.v33i01.33017884.

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Conventional topic models suffer from a severe sparsity problem when facing extremely short texts such as social media posts. The family of Dirichlet multinomial mixture (DMM) can handle the sparsity problem, however, they are still very sensitive to ordinary and noisy words, resulting in inaccurate topic representations at the document level. In this paper, we alleviate this problem by preserving local neighborhood structure of short texts, enabling to spread topical signals among neighboring documents, so as to correct the inaccurate topic representations. This is achieved by using variation

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Nguyen, Dat Quoc, Richard Billingsley, Lan Du, and Mark Johnson. "Improving Topic Models with Latent Feature Word Representations." Transactions of the Association for Computational Linguistics 3 (December 2015): 299–313. http://dx.doi.org/10.1162/tacl_a_00140.

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Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document cluste

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Liu, Lin, Lin Tang, Xin Jin, and Wei Zhou. "A Multi-Label Supervised Topic Model Conditioned on Arbitrary Features for Gene Function Prediction." Genes 10, no. 1 (2019): 57. http://dx.doi.org/10.3390/genes10010057.

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With the continuous accumulation of biological data, more and more machine learning algorithms have been introduced into the field of gene function prediction, which has great significance in decoding the secret of life. Recently, a multi-label supervised topic model named labeled latent Dirichlet allocation (LLDA) has been applied to gene function prediction, and obtained more accurate and explainable predictions than conventional methods. Nonetheless, the LLDA model is only able to construct a bag of amino acid words as a classification feature, and does not support any other features, such

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Shahbazi, Zeinab, and Yung-Cheol Byun. "Topic prediction and knowledge discovery based on integrated topic modeling and deep neural networks approaches." Journal of Intelligent & Fuzzy Systems 41, no. 1 (2021): 2441–57. http://dx.doi.org/10.3233/jifs-202545.

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Understanding the real-world short texts become an essential task in the recent research area. The document deduction analysis and latent coherent topic named as the important aspect of this process. Latent Dirichlet Allocation (LDA) and Probabilistic Latent Semantic Analysis (PLSA) are suggested to model huge information and documents. This type of contexts’ main problem is the information limitation, words relationship, sparsity, and knowledge extraction. The knowledge discovery and machine learning techniques integrated with topic modeling were proposed to overcome this issue. The knowledge

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Dall’Olio, Daniele, Eric Sträng, Amin T. Turki, et al. "Covering Hierarchical Dirichlet Mixture Models on binary data to enhance genomic stratifications in onco-hematology." PLOS Computational Biology 20, no. 2 (2024): e1011299. http://dx.doi.org/10.1371/journal.pcbi.1011299.

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Onco-hematological studies are increasingly adopting statistical mixture models to support the advancement of the genomically-driven classification systems for blood cancer. Targeting enhanced patients stratification based on the sole role of molecular biology attracted much interest and contributes to bring personalized medicine closer to reality. In onco-hematology, Hierarchical Dirichlet Mixture Models (HDMM) have become one of the preferred method to cluster the genomics data, that include the presence or absence of gene mutations and cytogenetics anomalies, into components. This work unfo

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Altarturi, Hamza H. M., Muntadher Saadoon, and Nor Badrul Anuar. "Web content topic modeling using LDA and HTML tags." PeerJ Computer Science 9 (July 11, 2023): e1459. http://dx.doi.org/10.7717/peerj-cs.1459.

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An immense volume of digital documents exists online and offline with content that can offer useful information and insights. Utilizing topic modeling enhances the analysis and understanding of digital documents. Topic modeling discovers latent semantic structures or topics within a set of digital textual documents. The Internet of Things, Blockchain, recommender system, and search engine optimization applications use topic modeling to handle data mining tasks, such as classification and clustering. The usefulness of topic models depends on the quality of resulting term patterns and topics wit

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Nandram, Balgobin. "A Bayesian Approach to Linking a Survey and a Census via Small Areas." Stats 4, no. 2 (2021): 509–28. http://dx.doi.org/10.3390/stats4020031.

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We predict the finite population proportion of a small area when individual-level data are available from a survey and more extensive household-level (not individual-level) data (covariates but not responses) are available from a census. The census and the survey consist of the same strata and primary sampling units (PSU, or wards) that are matched, but the households are not matched. There are some common covariates at the household level in the survey and the census and these covariates are used to link the households within wards. There are also covariates at the ward level, and the wards a

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Dindar, Duygu Altinok, Madeline Krieger, Amy Palma, et al. "Abstract 757: Integrating microbiome analysis and multi-modal data to identify high-risk population for esophageal adenocarcinoma." Cancer Research 84, no. 6_Supplement (2024): 757. http://dx.doi.org/10.1158/1538-7445.am2024-757.

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Abstract INTRODUCTION: Esophageal Adenocarcinoma (EAC) represents a pressing public health concern with a rising incidence and a staggering 5-year survival rate of less than 20%, emphasizing the critical need for early detection. Barrett's Esophagus (BE) serves as a well-established precursor to EAC, presenting an opportunity for proactive screening. The salivary microbiome provides a novel opportunity to identify a high-risk population with early-stage esophageal diseases. Here, we assessed whether the oral microbiome, in combination with lifestyle and clinical data, is associated with esopha

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Doubleday, Kevin, Daniel Gaile, Ravi Vijaya-Satya, et al. "Abstract 5015: Precision profile simulation study for a next generation sequencing bTMB assay." Cancer Research 82, no. 12_Supplement (2022): 5015. http://dx.doi.org/10.1158/1538-7445.am2022-5015.

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Abstract Background: Precision profile simulations (PPS) can be used to assess variability of biomarker profiles and provide valuable insight into assay performance, especially when reliable precision estimates can not be obtained empirically due to scarcity of representative samples or insufficient materials per sample. A PPS was conducted for the GuardantOMNI assay to characterize the expected variability in blood tumor mutational burden (bTMB) score across a representative range of expected bTMB scores in clinical samples. The simulations were aligned to, but not completely prescribed by, t

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20

Wilson, Jeffrey R., and Grace S. C. Chen. "Dirichlet-multinomial Model with Varying Response Rates over Time." Journal of Data Science 5, no. 3 (2021): 413–23. http://dx.doi.org/10.6339/jds.2007.05(3).334.

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21

Duncan, Kristin A., and Jonathan L. Wilson. "A Multinomial-Dirichlet Model for Analysis of Competing Hypotheses." Risk Analysis 28, no. 6 (2008): 1699–709. http://dx.doi.org/10.1111/j.1539-6924.2008.01139.x.

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22

Afroz, Farzana, and Zillur Rahman Shabuz. "Comparison Between Two Multinomial Overdispersion Models Through Simulation." Dhaka University Journal of Science 68, no. 1 (2020): 45–48. http://dx.doi.org/10.3329/dujs.v68i1.54596.

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A key assumption when using the multinomial distribution is that the observations are independent. In many practical situations, the observations could be correlated or clustered and the probabilities within each cluster might vary, which may lead to overdispersion. In this paper we discuss two well-known approaches to model overdispersed multinomial data, the Dirichlet-multinomial model and the finite-mixture model. The difference between these two models has been illustrated via simulation study. The forest pollen data is considered as a practical example of overdisperse multinomial data. Th

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23

Obiorah ,, Philip, Friday Onuodu, and Batholowmeo Eke. "Topic Modeling Using Latent Dirichlet Allocation & Multinomial Logistic Regression." Advances in Multidisciplinary and scientific Research Journal Publication 10, no. 4 (2022): 99–112. http://dx.doi.org/10.22624/aims/digital/v10n4p11a.

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Unsupervised categorization for datasets has benefits, but not without a few difficulties. Unsupervised algorithms cluster groups of documents in an unsupervised fashion, and often output findings as vectors containing distributions of words clustered according to their probability of occurring together. Additionally, this technique requires human or domain expert interpretation in order to correctly identify clusters of words as belonging to a certain topic. We propose combining Latent Dirichlet Allocation (LDA) with multi-class Logistic Regression for topic modelling as a multi-step classifi

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24

Bernard, Jean-Marc. "An introduction to the imprecise Dirichlet model for multinomial data." International Journal of Approximate Reasoning 39, no. 2-3 (2005): 123–50. http://dx.doi.org/10.1016/j.ijar.2004.10.002.

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25

Huang, Ruizhang, Weijia Xu, Yongbin Qin, and Yanping Chen. "Hierarchical Dirichlet Multinomial Allocation Model for Multi-Source Document Clustering." IEEE Access 8 (2020): 109917–27. http://dx.doi.org/10.1109/access.2020.3002107.

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Guimarães, Paulo. "A Simple Approach to Fit the Beta-binomial Model." Stata Journal: Promoting communications on statistics and Stata 5, no. 3 (2005): 385–94. http://dx.doi.org/10.1177/1536867x0500500307.

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In this paper, I show how to estimate the parameters of the beta-binomial distribution and its multivariate generalization, the Dirichlet-multinomial distribution. This approach involves no additional programming, as it relies on an existing Stata command used for overdispersed count panel data. Including covariates to allow for regression models based in these distributions is straightforward.

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Freitas, Silvia Maria de, Lida Fallah, Clarice G. B. Demétrio, and John P. Hinde. "Overdispersion Models for Clustered Toxicological Data in a Bioassay of Entomopathogenic Fungus." Brazilian Journal of Biometrics 40, no. 4 (2023): 490–509. http://dx.doi.org/10.28951/bjb.v40i4.647.

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We consider discrete mortality data for groups of individuals observed over time. The fitting of cumulative mortality curves as a function of time involves the longitudinal modelling of the multinomial response. Typically such data exhibit overdispersion, that is greater variation than predicted by the multinomial distribution. To model the extra-multinomial variation (overdispersion) we consider a Dirichlet-multinomial model, a random intercept model and a random intercept and slope model. We construct asymptotic and robust covariance matrix estimators for the regression parameter standard er

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Hrafnkelsson, Birgir, and Gunnar Stefánsson. "A model for categorical length data from groundfish surveys." Canadian Journal of Fisheries and Aquatic Sciences 61, no. 7 (2004): 1135–42. http://dx.doi.org/10.1139/f04-049.

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An extension of the multinomial model of counts is presented to account for overdispersion and different correlation structure. Such models are needed in biological applications such as the analysis of length measurements from surveys of heterogeneous populations used for assessments of marine resources. One of the goals of such a survey is to estimate the length distribution of each species within a particular area. Using data on Atlantic cod (Gadus morhua) in Icelandic waters, it is demonstrated that the assumptions used in practice for categorical length data are seriously violated. The len

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Stein, N. M., and X. L. Meng. "Practical perfect sampling using composite bounding chains: the Dirichlet-multinomial model." Biometrika 100, no. 4 (2013): 817–30. http://dx.doi.org/10.1093/biomet/ast024.

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Yang, Ni, and Youpeng Zhang. "Railway Fault Text Clustering Method Using an Improved Dirichlet Multinomial Mixture Model." Mathematical Problems in Engineering 2022 (July 4, 2022): 1–12. http://dx.doi.org/10.1155/2022/7882396.

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Railway signal equipment fault data (RSEFD) are one of the issues with in-depth traffic big data analysis throughout the life cycle of intelligent transportation. In the course of daily operation and maintenance, the railway electrical maintenance department records equipment malfunction information in a natural language. The data have the characteristics of strong professionalism, short text, unbalanced category, and low efficiency of manual analysis and processing. How to effectively mine the information contained in these fault texts to provide help for on-site operation and maintenance pla

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Ding, Yunfei, and Robert F. Harrison. "A sparse multinomial probit model for classification." Pattern Analysis and Applications 14, no. 1 (2010): 47–55. http://dx.doi.org/10.1007/s10044-010-0177-7.

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32

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

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Danaher, Peter J. "A Log-Linear Model for Predicting Magazine Audiences." Journal of Marketing Research 25, no. 4 (1988): 356–62. http://dx.doi.org/10.1177/002224378802500403.

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A log-linear model for predicting magazine exposure distributions is developed and its parameters are estimated by the maximum likelihood technique. The log-linear model is compared empirically with the best-found model for equal-insertion schedules, one of Leckenby and Kishi's Dirichlet multinomial models. For unequal-insertion schedules the log-linear model is compared with the popular Metheringham beta-binomial model. The results show that the log-linear model has significantly smaller prediction errors than either of the other models.

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Menéndez, M. L., D. Morales, L. Pardo, and I. Vajda. "About divergence-based goodness-of-fit tests in the dirichlet-multinomial model." Communications in Statistics - Theory and Methods 25, no. 5 (1996): 1119–33. http://dx.doi.org/10.1080/03610929608831752.

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ENNIS, DANIEL M., and JIAN BI. "THE DIRICHLET-MULTINOMIAL MODEL: ACCOUNTING FOR INTER-TRIAL VARIATION IN REPLICATED RATINGS." Journal of Sensory Studies 14, no. 3 (1999): 321–45. http://dx.doi.org/10.1111/j.1745-459x.1999.tb00120.x.

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MATSUMURA, ELLA MAE, KAM-WAH TSUI, and WING-KEUNG WONG. "An extended multinomial-Dirichlet model for error bounds for dollar-unit sampling." Contemporary Accounting Research 6, no. 2 (1990): 485–500. http://dx.doi.org/10.1111/j.1911-3846.1990.tb00770.x.

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Jiang, C. J., and C. Clark Cockerham. "Use of the Multinomial Dirichlet Model for Analysis of Subdivided Genetic Populations." Genetics 115, no. 2 (1987): 363–66. http://dx.doi.org/10.1093/genetics/115.2.363.

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ABSTRACT The distribution found by compounding the multinomial distribution with the Dirichlet distribution has been suggested as a basis for the estimation of parameters in subdivided populations, in particular of the "correlation between genotypes" within subpopulations. It is shown that the estimators deriving from these procedures perform poorly when the data are generated by the classical Wright drift model of subdivided populations. This conclusion suggests that the compound distribution estimation approach does not provide a good estimation procedure for real populations which are reaso

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Tang, Zheng-Zheng, and Guanhua Chen. "Zero-inflated generalized Dirichlet multinomial regression model for microbiome compositional data analysis." Biostatistics 20, no. 4 (2018): 698–713. http://dx.doi.org/10.1093/biostatistics/kxy025.

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Summary There is heightened interest in using high-throughput sequencing technologies to quantify abundances of microbial taxa and linking the abundance to human diseases and traits. Proper modeling of multivariate taxon counts is essential to the power of detecting this association. Existing models are limited in handling excessive zero observations in taxon counts and in flexibly accommodating complex correlation structures and dispersion patterns among taxa. In this article, we develop a new probability distribution, zero-inflated generalized Dirichlet multinomial (ZIGDM), that overcomes th

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Yoshida, Takahiro, Ryohei Hisano, and Takaaki Ohnishi. "Gaussian hierarchical latent Dirichlet allocation: Bringing polysemy back." PLOS ONE 18, no. 7 (2023): e0288274. http://dx.doi.org/10.1371/journal.pone.0288274.

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Topic models are widely used to discover the latent representation of a set of documents. The two canonical models are latent Dirichlet allocation, and Gaussian latent Dirichlet allocation, where the former uses multinomial distributions over words, and the latter uses multivariate Gaussian distributions over pre-trained word embedding vectors as the latent topic representations, respectively. Compared with latent Dirichlet allocation, Gaussian latent Dirichlet allocation is limited in the sense that it does not capture the polysemy of a word such as “bank.” In this paper, we show that Gaussia

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Ortego, M. I., and J. J. Egozcue. "Bayesian estimation of the orthogonal decomposition of a contingency table." Austrian Journal of Statistics 45, no. 4 (2016): 45–56. http://dx.doi.org/10.17713/ajs.v45i4.136.

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In a multinomial sampling, contingency tables can be parametrized by probabilities of each cell. These probabilities constitute the joint probability function of two or more discrete random variables. These probability tables have been previously studied from a compositional point of view. The compositional analysis of probability tables ensures coherence when analysing sub-tables. The main results are:(1) given a probability table, the closest independent probability table is the product of their geometric marginals;(2) the probability table can be orthogonally decomposed into an independent

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Nowicka, Malgorzata, and Mark D. Robinson. "DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics." F1000Research 5 (December 6, 2016): 1356. http://dx.doi.org/10.12688/f1000research.8900.2.

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There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where differences (e.g. between normal and disease state) in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trait loci (sQTL) will help to characterize the effects of genet

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Chen, Liuyuan, Jie Yang, Juntao Li, and Xiaoyu Wang. "Multinomial Regression with Elastic Net Penalty and Its Grouping Effect in Gene Selection." Abstract and Applied Analysis 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/569501.

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For the multiclass classification problem of microarray data, a new optimization model named multinomial regression with the elastic net penalty was proposed in this paper. By combining the multinomial likeliyhood loss and the multiclass elastic net penalty, the optimization model was constructed, which was proved to encourage a grouping effect in gene selection for multiclass classification.

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Nowicka, Malgorzata, and Mark D. Robinson. "DRIMSeq: a Dirichlet-multinomial framework for multivariate count outcomes in genomics." F1000Research 5 (June 13, 2016): 1356. http://dx.doi.org/10.12688/f1000research.8900.1.

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There are many instances in genomics data analyses where measurements are made on a multivariate response. For example, alternative splicing can lead to multiple expressed isoforms from the same primary transcript. There are situations where the total abundance of gene expression does not change (e.g. between normal and disease state), but differences in the relative ratio of expressed isoforms may have significant phenotypic consequences or lead to prognostic capabilities. Similarly, knowledge of single nucleotide polymorphisms (SNPs) that affect splicing, so-called splicing quantitative trai

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Wang, Tao, and Hongyu Zhao. "A Dirichlet-tree multinomial regression model for associating dietary nutrients with gut microorganisms." Biometrics 73, no. 3 (2017): 792–801. http://dx.doi.org/10.1111/biom.12654.

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Tang, Yunfan, Li Ma, and Dan L. Nicolae. "A phylogenetic scan test on a Dirichlet-tree multinomial model for microbiome data." Annals of Applied Statistics 12, no. 1 (2018): 1–26. http://dx.doi.org/10.1214/17-aoas1086.

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Tervonen, T., F. Pignatti, and D. Postmus. "PRM193 - FROM INDIVIDUAL PATIENT TO POPULATION PREFERENCES: MULTINOMIAL LOGIT MODEL VS DIRICHLET DISTRIBUTION." Value in Health 21 (October 2018): S389. http://dx.doi.org/10.1016/j.jval.2018.09.2311.

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Li, Kang, Xian-ming Shi, Juan Li, Mei Zhao, and Chunhua Zeng. "Bayesian Estimation of Ammunition Demand Based on Multinomial Distribution." Discrete Dynamics in Nature and Society 2021 (April 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/5575335.

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In view of the small sample size of combat ammunition trial data and the difficulty of forecasting the demand for combat ammunition, a Bayesian inference method based on multinomial distribution is proposed. Firstly, considering the different damage grades of ammunition hitting targets, the damage results are approximated as multinomial distribution, and a Bayesian inference model of ammunition demand based on multinomial distribution is established, which provides a theoretical basis for forecasting the ammunition demand of multigrade damage under the condition of small samples. Secondly, the

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Tervonen, Tommi, Francesco Pignatti, and Douwe Postmus. "From Individual to Population Preferences: Comparison of Discrete Choice and Dirichlet Models for Treatment Benefit-Risk Tradeoffs." Medical Decision Making 39, no. 7 (2019): 879–85. http://dx.doi.org/10.1177/0272989x19873630.

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Introduction. The Dirichlet distribution has been proposed for representing preference heterogeneity, but there is limited evidence on its suitability for modeling population preferences on treatment benefits and risks. Methods. We conducted a simulation study to compare how the Dirichlet and standard discrete choice models (multinomial logit [MNL] and mixed logit [MXL]) differ in their convergence to stable estimates of population benefit-risk preferences. The source data consisted of individual-level tradeoffs from an existing 3-attribute patient preference study ( N = 560). The Dirichlet po

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Retno, Kusumaningrum, Adhy Satriyo, and Suryono. "WCLOUDVIZ: Word Cloud Visualization of Indonesian News Articles Classification Based on Latent Dirichlet Allocation." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 4 (2018): 1752–59. https://doi.org/10.12928/TELKOMNIKA.v16i4.8194.

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Latent Dirichlet Allocation (LDA) is a widely implemented approach for extracting hidden topics in documents generated by soft clustering of a word based on document co-occurrence as a multinomial probability distribution over terms. Therefore, several visualizations have been developed, such as matrices design, text-based design, tree design, parallel coordinates, and force-directed graphs. Furthermore, based on a set of documents representing a class (category), we can implement classification task by comparing topic proportion for each class and topic proportion for the testing document by

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Ay, Fahrettin, Gökhan İnce, Mustafa E. Kamaşak, and K. Yavuz Ekşi. "Classification of pulsars with Dirichlet process Gaussian mixture model." Monthly Notices of the Royal Astronomical Society 493, no. 1 (2020): 713–22. http://dx.doi.org/10.1093/mnras/staa154.

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ABSTRACT Young isolated neutron stars (INSs) most commonly manifest themselves as rotationally powered pulsars that involve conventional radio pulsars as well as gamma-ray pulsars and rotating radio transients. Some other young INS families manifest themselves as anomalous X-ray pulsars and soft gamma-ray repeaters that are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying super-strong fields. Yet some other young INSs are identified as central compact objects and X-ray dim isolated neutron stars that are cooling objects powered by their thermal energy. Olde

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