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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
Bayesian curve fitting and clustering with Dirichlet process mixture models for microarray data
Ju-Hyun Park,Minjung Kyung +1 more
TL;DR: To group sequences of similar molecular functions, a Bayesian Dirichlet process mixture of linear regression models with a Fourier series for the regression coefficients is proposed, for each of which a spike and slab prior is assumed.
Journal ArticleDOI
An area-specific stick breaking process for spatial data
TL;DR: This paper develops an area-specific stick breaking process for distributions of random effects with the spatially-dependent weights arising from the block averaging of underlying continuous surfaces that is noticeably flexible in effectively capturing heterogeneity in spatial dependency across areas.
Proceedings ArticleDOI
MCNC: Multi-Channel Nonparametric Clustering from heterogeneous data
TL;DR: A BNP framework, termed MCNC, which has the ability to discover co-patterns from multiple sources; explore multi-channel data simultaneously and treat them equally; automatically identify a suitable number of patterns from data; and handle missing data is presented.
Posted Content
Multiple repairable systems under dependent competing risks with nonparametric Frailty
Marco Pollo Almeida,Rafael S. Paixão,Pedro Luiz Ramos,Vera Tomazella,Francisco Louzada,Ricardo S. Ehlers +5 more
TL;DR: A frailty-induced dependence approach is proposed to incorporate the dependence among the cause-specific recurrent processes in the dependent competing risks model to offer more flexibility and to provide consistent estimates for the PLP model, as well as insights about heterogeneity among the systems.
Proceedings ArticleDOI
Dirichlet process mixture models with multiple modalities
John Paisley,Lawrence Carin +1 more
TL;DR: A general MCMC sampling scheme is provided and observations containing multiple, incompatible pieces of information can be mixed upon, allowing for all information to inform the final clustering result.
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.