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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
Citations
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Journal ArticleDOI
Modeling with Normalized Random Measure Mixture Models
TL;DR: The Dirichlet process mixture model and more general mixtures based on discrete random probability measures have been shown to be flexible and accurate models for density estimation and clustering.
Book ChapterDOI
Nonparametric Bayes methods using predictive updating
TL;DR: In this paper, the Dirichlet process prior is used to estimate approximate nonparametric Bayes estimates in a wide range of models using a simple recursive algorithm, and applied to an interval censoring problem and a Markov chain mixture model.
Journal ArticleDOI
SMAUG: Analyzing single-molecule tracks with nonparametric Bayesian statistics.
Joshua D. Karslake,Eric D. Donarski,Sarah A. Shelby,Lucas M. Demey,Victor J. DiRita,Sarah L. Veatch,Julie S. Biteen +6 more
TL;DR: The Single-Molecule Analysis by Unsupervised Gibbs sampling (SMAUG) algorithm is introduced, which uses nonparametric Bayesian statistics to uncover the whole range of information contained within a single-particle trajectory dataset.
Proceedings ArticleDOI
Nonparametric discovery of human routines from sensor data
TL;DR: Experimental results show that the nonparametric framework presented can automatically learn the appropriate model parameters from sensor data without any form of model selection procedure and can outperform traditional parametric approaches for human routine discovery tasks.
Proceedings ArticleDOI
Semiotic prediction of driving behavior using unsupervised double articulation analyzer
TL;DR: A novel semiotic prediction method is proposed for driving behavior based on double articulation structure by extending nonparametric Bayesian unsupervised morphological analyzer and achieving long-term prediction 2-6 times longer than some conventional methods.
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.