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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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Asymptotic inference for mixture models by using data‐dependent priors
TL;DR: In this paper, the authors show that data-dependent priors are the only priors that produce intervals with second-order correct frequentist coverage, and that the resulting posterior is the product of a fixed prior and a pseudolikelihood.
Journal ArticleDOI
Model-Based Cognitive Neuroscience Approaches to Computational Psychiatry Clustering and Classification
TL;DR: In this article, the authors highlight efforts to overcome current challenges by focusing on the emerging field of computational psychiatry, which might enable the field to move from a symposium to a real world.
Journal ArticleDOI
A multivariate semiparametric bayesian spatial modeling framework for hurricane surface wind fields
TL;DR: In this paper, a new Bayesian multivariate spatial statistical modeling framework is introduced combining data with physical knowledge about the wind fields to improve the estimation of the wind vectors, which can capture the asymmetric and dynamic nature of a hurricane.
Journal ArticleDOI
Variational Inference for Infinite Mixtures of Gaussian Processes With Applications to Traffic Flow Prediction
Shiliang Sun,Xin Xu +1 more
TL;DR: This paper proposes a new variational approximation for infinite mixtures of Gaussian processes that uses variational inference and a truncated stick-breaking representation of the Dirichlet process to approximate the posterior of hidden variables involved in the model.
Proceedings ArticleDOI
The dynamic hierarchical Dirichlet process
TL;DR: The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving statistical properties of sequential data sets, and a relatively simple Markov Chain Monte Carlo sampler is developed.
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.