Results 211 to 220 of about 4,505,818 (240)

A Dirichlet process mixture of dirichlet distributions for classification and prediction

2008 IEEE Workshop on Machine Learning for Signal Processing, 2008
A significant problem in clustering is the determination of the number of classes which best describes the data. This paper proposes a learning approach based on both Dirichlet process and Dirichlet distribution which provide flexible nonparametric Bayesian framework for non-Gaussian data clustering.
Nizar Bouguila, Djemel Ziou
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On the Inverted Dirichlet Distribution

Communications in Statistics - Theory and Methods, 2009
In this work, we give a representation of the mean expected value of many functionals of inverted Dirichlet distributions in terms of the mean of independent gamma random variables. Some remarkable properties are developed and illustrated. The Gibbs version of the inverted Dirichlet distribution with a selection parameter is considered.
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The Dirichlet Distribution and Process through Neutralities

Journal of Theoretical Probability, 2007
Some new characterizations are given for the Dirichlet distribution and Dirichlet process in terms of neutrality and neutrality to the right, e.g., Theorem 8: if \((F(t))_{t\in\mathbb R}\) is a stochastic process such that its trajectories are a.s. CDFs, \(( F(t))_{t\in\mathbb R}\) is neutral to the right and \(1-F(t_n)\) is neutral in \((F(t_1), F(t_2)
Bobecka, Konstancja, Wesołowski, Jacek
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The Poisson-Dirichlet distribution

1992
Abstract The parameters α j may take any strictly positive values, and the character of the distribution changes markedly as these vary. If α j = 1 for all j we have the uniform distribution on Δ n If the α j = are large, (9.3) concentrates probability well away from the boundaries of Δ n, corresponding to distributions p which are ...
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Interpreting Belief Functions as Dirichlet Distributions

2007
Traditional Dempster Shafer belief theory does not provide a simple method for judging the effect of statistical and probabilistic data on belief functions and vice versa. This puts belief theory in isolation from probability theory and hinders fertile cross-disciplinary developments, both from a theoretic and an application point of view.
Audun Jøsang, Zied Elouedi
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The Poisson–Dirichlet Distribution

2010
The focus of this chapter is the Poisson–Dirichlet distribution, the central topic of this book. We introduce this distribution and discuss various models that give rise to it. Following Kingman (J. Roy. Statist. Soc. B 37:1–22, 1975), the distribution is constructed through the gamma process. An alternative construction in (R. Arratia, A.D.
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On the distribution of points in a poisson dirichlet process

Journal of Applied Probability, 1988
A probability density function important in the Poisson Dirichlet process of population genetics is studied. An accurate computational algorithm is given for this density and for the marginal distributions of the points in the Poisson Dirichlet process. The distribution of the maximal point of the process is tabulated.
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Topic Modeling Using Latent Dirichlet allocation

ACM Computing Surveys, 2022
Apurva Shāh
exaly  

Estimating Normal Means with a Dirichlet Process Prior

Journal of the American Statistical Association, 1994
Michael Escobar
exaly  

The dirichlet problem for a complex Monge-Amp�re equation

Inventiones Mathematicae, 1976
Eric Bedford
exaly