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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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Journal ArticleDOI
A semiparametric bayesian approach to network modelling using dirichlet process prior distributions
TL;DR: In this paper, the authors consider the use of Dirichlet process prior distributions in the statistical analysis of network data and highlight the advantages of avoiding the parametric specifications for distributions, which are rarely known, and of facilitating a clustering effect, which is often applicable to network nodes.
Journal ArticleDOI
Copula based factorization in Bayesian multivariate infinite mixture models
TL;DR: This paper proposes a factorization scheme of multivariate dependence structures based on the copula modeling framework, whereby each marginal dimension in the mixing parameter space is modeled separately and the marginals are then linked by a nonparametric random copula function.
Posted Content
A constructive definition of the beta process
John Paisley,Michael I. Jordan +1 more
TL;DR: A construction of the beta process that allows for the atoms with significant measure to be drawn first is derived and is referred to as a stick-breaking construction and an efficient sampling algorithm for beta-Bernoulli and beta-negative binomial process models is presented.
Proceedings Article
Streaming variational inference for dirichlet process mixtures
TL;DR: This paper presents two truncation-free variational algorithms, one for mix-membership inference called TFVB (truncation- free variational Bayes), and the other for hard clustering inference calledTFME (trUNCUBE), which further developed a streaming learning framework for the popular Dirichlet process mixture models.
Journal ArticleDOI
Bayesian Analysis of Ordinal Survey Data Using the Dirichlet Process to Account for Respondent Personality Traits
TL;DR: A Bayesian latent variable model used to analyze ordinal response survey data by taking into account the characteristics of respondents, which is applied to student survey data in course evaluations.
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.