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A constructive definition of dirichlet priors
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The article was published on 1991-01-01 and is currently open access. It has received 1560 citations till now. The article focuses on the topics: Hierarchical Dirichlet process & Constructive.read more
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Book ChapterDOI
Real time event detection in twitter
TL;DR: A mixture Gaussian model for bursty word extraction in Twitter and then a novel time-dependent HDP model for new topic detection, which can grasp new events, the location and the time an event becomes bursty promptly and accurately are proposed.
Journal ArticleDOI
Infinite Hidden Conditional Random Fields for Human Behavior Analysis
TL;DR: The infinite HCRF (iHCRF), which is a nonparametric model based on hierarchical Dirichlet processes and is capable of automatically learning the optimal number of hidden states for a classification task, is presented.
Proceedings ArticleDOI
Adding More Languages Improves Unsupervised Multilingual Part-of-Speech Tagging: a Bayesian Non-Parametric Approach
TL;DR: A non-parametric Bayesian model is proposed that connects related tagging decisions across languages through the use of multilingual latent variables and shows that performance improves steadily as the number of languages increases.
Journal ArticleDOI
Estimation in Dirichlet random effects models
TL;DR: The Gibbs sampler is developed, which is easily extended to a generalized linear mixed model with a probit link function, and is shown to be an improvement, in terms of operator norm and efficiency, over other commonly used MCMC algorithms.
Journal ArticleDOI
Bayesian Semiparametric Multivariate GARCH Modeling
Mark J. Jensen,John M. Maheu +1 more
TL;DR: In this paper, a Bayesian nonparametric modeling approach for the return distribution in multivariate GARCH models is proposed, where an infinite mixture of multivariate normals is given a flexible Dirichlet process prior.
References
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Journal ArticleDOI
A Bayesian Analysis of Some Nonparametric Problems
TL;DR: In this article, a class of prior distributions, called Dirichlet process priors, is proposed for nonparametric problems, for which treatment of many non-parametric statistical problems may be carried out, yielding results that are comparable to the classical theory.
Journal ArticleDOI
Ferguson Distributions Via Polya Urn Schemes
TL;DR: In this article, it was shown that a random probability measure P* on X has a Ferguson distribution with parameter p if for every finite partition (B1, *. *, B) of X, the vector p*(B,), * * *, p *(B) has a Dirichlet distribution with parameters (Bj), *--, cp(B,) (when p(B), = 0, this means p*) = 0 with probability 1).
Journal ArticleDOI
Implicit renewal theory and tails of solutions of random equations
TL;DR: For the solutions of certain random equations, or equivalently the stationary solutions of the random recurrences, the distribution tails are evaluated by renewal-theoretic methods as mentioned in this paper.
Book ChapterDOI
Bayesian density estimation by mixtures of normal distributions
TL;DR: In this article, a mixture of a countable number of normal distributions is used to estimate a density f(x) on the real line, which is then used for kernel estimation.
Journal ArticleDOI
On the Asymptotic Behavior of Bayes' Estimates in the Discrete Case
TL;DR: In this article, it was shown that the posterior probability converges to point mass at the true parameter value among almost all sample sequences (for short, the posterior is consistent; see Definition 1) exactly for parameter values in the topological carrier of the prior.