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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
Disclosure Risk Evaluation for Fully Synthetic Categorical Data
TL;DR: This work uses a “worst-case” scenario of an intruder knowing all but one of the records in the confidential data to compute probability distributions of unknown confidential data values given the synthetic data and assumptions about intruder knowledge.
Journal ArticleDOI
On selecting a prior for the precision parameter of Dirichlet process mixture models
TL;DR: In this paper, an approach is developed for computing a prior for the precision parameter α that can be used in the presence or absence of prior information about the level of clustering.
Proceedings ArticleDOI
Hierarchical Taxonomy Aware Network Embedding
TL;DR: NetHiex is proposed, a NETwork embedding model that captures the latent HIErarchical taXonomy and employs the nested Chinese restaurant process to guide the search of the most plausible hierarchical taxonomy.
Journal ArticleDOI
A Bayesian nonparametric approach for uncovering rat hippocampal population codes during spatial navigation.
TL;DR: The Bayesian nonparametric HDP-HMM method can efficiently perform model selection and identify model parameters, which can used for modeling latent-state neuronal population dynamics.
Journal ArticleDOI
An enriched conjugate prior for Bayesian nonparametric inference
TL;DR: In this article, an enriched conjugate prior is proposed to model uncertainty on the marginal and conditionals of the Dirichlet process. But it does not address an analogous lack of exibility of standard conju- gate priors in a parametric setting.
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.