Relevant bibliographies by topics / Dirichlet distribution

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Academic literature on the topic 'Dirichlet distribution'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 March 2023

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Dissertations / Theses on the topic "Dirichlet distribution"

1

Katkuri, Jaipal. "Application of Dirichlet Distribution for Polytopic Model Estimation." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1210.

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The polytopic model (PM) structure is often used in the areas of automatic control and fault detection and isolation (FDI). It is an alternative to the multiple model approach which explicitly allows for interpolation among local models. This thesis proposes a novel approach to PM estimation by modeling the set of PM weights as a random vector with Dirichlet Distribution (DD). A new approximate (adaptive) PM estimator, referred to as a Quasi-Bayesian Adaptive Kalman Filter (QBAKF) is derived and implemented. The model weights and state estimation in the QBAKF is performed adaptively by a simpl
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2

Li, Yi. "Goodness-of-Fit Tests For Dirichlet Distributions With Applications." Bowling Green State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1435003723.

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Amandine, Saldana. "Séries de Dirichlet à deux variables et distribution des valeurs de fonctions arithmétiques." Phd thesis, Université des Sciences et Technologie de Lille - Lille I, 2009. http://tel.archives-ouvertes.fr/tel-00426287.

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Nous traitons deux problèmes liés aux séries de Dirichlet. Nous étudions d'abord le prolongement analytique d'une certaine classe de séries de Dirichlet à deux variables : g(s_1,s_2,a,r)=∑ (d≥1) r(d)a(d)^{-s_1}d^{-s_2}, où a(d) est une fonction multiplicative strictement positive et r(d) est une fonction multiplicative. Nous démontrons, sous certaines hypothèses, un théorème général qui permet d'approcher cette série de Dirichlet par une série connue, modulo une autre série pour laquelle nous obtenons des majorations très précises. Nous utilisons ensuite cet outil pour obtenir des résultats qu
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4

Saldana, Amandine. "Séries de Dirichlet à deux variables et distribution des valeurs de fonctions arithmétiques." Thesis, Lille 1, 2009. http://www.theses.fr/2009LIL10026/document.

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Nous traitons deux problèmes liés aux séries de Dirichlet. Nous étudions d'abord le prolongement analytique d'une certaine classe de séries de Dirichlet à deux variables: g(s_1,s_2,a,r) = somme_d=1 r(d) / a(d)s1ds2, où a(d) est une fonction multiplicative strictement positive et r(d) est une fonction multiplicative. Nous démontrons, sous certaines hypothèses, un théorème général qui permet d'approcher cette série de Dirichlet par une série connue, modulo une autre série pour laquelle nous obtenons des majorations très précises. Nous utilisons ensuite cet outil pour obtenir des résultats quanti
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5

Etienne, Roland Jean [Verfasser]. "On the asymptotic distribution of the Dirichlet eigenvalues of fractal chains / Roland Jean Etienne." Siegen : Universitätsbibliothek der Universität Siegen, 2015. http://d-nb.info/1068362863/34.

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6

MATSUMOTO, Kohji. "An introduction to the value-distribution theory of zeta-functions." Šiauliai University, 2006. http://hdl.handle.net/2237/20445.

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7

Bouguila, Nizar. "Estimation et sélection non supervisées des mélanges basés sur la distribution de Dirichlet et applications." Thèse, Université de Sherbrooke, 2006. http://savoirs.usherbrooke.ca/handle/11143/5059.

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Cette thèse à publication présente l’ensemble de nos contributions relatives aux mélanges basés sur la famille de Dirichlet-Liouville. La thèse est divisée en six chapitres. Dans le premier chapitre, nous introduisons le mélange de Dirichlet tout en proposant un algorithme pour l’estimation de ses paramètres dans un espace Riemannien. Cet algorithme est validé par une application de résumé d’une base de données d’images et par une autre application qui consiste à modéliser la couleur de la peau dans des images. Le deuxième chapitre aborde la modélisation des données discrètes qui sont très pré
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8

Maier, Marco J. "DirichletReg: Dirichlet Regression for Compositional Data in R." WU Vienna University of Economics and Business, 2014. http://epub.wu.ac.at/4077/1/Report125.pdf.

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Dirichlet regression models can be used to analyze a set of variables lying in a bounded interval that sum up to a constant (e.g., proportions, rates, compositions, etc.) exhibiting skewness and heteroscedasticity, without having to transform the data. There are two parametrization for the presented model, one using the common Dirichlet distribution's alpha parameters, and a reparametrization of the alpha's to set up a mean-and-dispersion-like model. By applying appropriate link-functions, a GLM-like framework is set up that allows for the analysis of such data in a straightforward and
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9

Costa, Eliardo Guimarães da. "Tamanho amostral para estimar a concentração de organismos em água de lastro: uma abordagem bayesiana." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-05072018-164225/.

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Metodologias para obtenção do tamanho amostral para estimar a concentração de organismos em água de lastro e verificar normas internacionais são desenvolvidas sob uma abordagem bayesiana. Consideramos os critérios da cobertura média, do tamanho médio e da minimização do custo total sob os modelos Poisson com distribuição a priori gama e binomial negativo com distribuição a priori Pearson Tipo VI. Além disso, consideramos um processo Dirichlet como distribuição a priori no modelo Poisson com o propósito de obter maior flexibilidade e robustez. Para fins de aplicação, implementamos rotinas compu
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10

Malsiner-Walli, Gertraud, Sylvia Frühwirth-Schnatter, and Bettina Grün. "Model-based clustering based on sparse finite Gaussian mixtures." Springer, 2016. http://dx.doi.org/10.1007/s11222-014-9500-2.

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In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian distributions, we present a joint approach to estimate the number of mixture components and identify cluster-relevant variables simultaneously as well as to obtain an identified model. Our approach consists in specifying sparse hierarchical priors on the mixture weights and component means. In a deliberately overfitting mixture model the sparse prior on the weights empties superfluous components during MCMC. A straightforward estimator for the true number of components is given by the most frequent number
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