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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.

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Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap.

TL;DR: This work presents a scalable Bayesian nonparametric learning routine that enables posterior sampling through the optimization of suitably randomized objective functions and is particularly adept at sampling from multimodal posterior distributions via a random restart mechanism.
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Unsupervised Profiling of Microglial Arbor Morphologies and Distribution Using a Nonparametric Bayesian Approach

TL;DR: A nonparametric Bayesian approach to unsupervised quantitative profiling of microglial activation states, and mapping their 3-D spatial distributions across extended brain tissue regions imaged by mosaiced confocal microscopy is presented.
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Where Did the Brownian Particle Go

TL;DR: In this paper, it was shown that for any Borel set of the path, the conditional probability that the projection of the original path is in the plane is at least 1 from the occupation measure of the projected path, and that both the range and the terminal point of the projection can be recovered with probability 1 from this occupation measure.

Hierarchical Dirichlet Process-Based Models For Discovery of Cross-species Mammalian Gene Expression

TL;DR: GeneProgram is a new unsupervised computational framework that uses expression data to simultaneously organize genes into overlapping programs and tissues into groups to produce maps of inter-species expression programs, which are sorted by generality scores that exploit the automatically learned groupings.
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Bayesian nonparametric models for ranked set sampling

TL;DR: This paper lays out a formal and natural Bayesian framework for RSS that is analogous to its frequentist justification, and that does not require the assumption of perfect ranking or use of any imperfect ranking models.
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.
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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.
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