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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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Trajectory Analysis and Semantic Region Modeling Using A Nonparametric
TL;DR: A novel nonparametric Bayesian model for trajectory analysis and semantic region modeling in surveillance settings, in an unsupervised way, that advances the existing hierarchical Dirichlet processes (HDP) language model.
Posted Content
Hierarchical Stochastic Block Model for Community Detection in Multiplex Networks.
TL;DR: A novel and efficient Bayesian model for community detection in multiplex networks using a hierarchical Dirichlet prior to model community labels across layers, allowing dependency in their structure and developing an efficient slice sampler for sampling the posterior distribution of the community labels as well as the link probabilities between communities.
Journal ArticleDOI
Improved and Scalable Online Learning of Spatial Concepts and Language Models with Mapping
TL;DR: In this paper, the authors proposed a novel online learning algorithm, called SpCoSLAM 2.0, for spatial concepts and lexical acquisition with high accuracy and scalability.
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Bayesian non-parametric modeling for integro-difference equations
TL;DR: It is shown that the spatial Dirichlet process mixture model outperforms several alternative models for the IDE kernel, including the state of the art in the IDE literature, that is, a Gaussian kernel with location-dependent parameters.
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Learning for non-stationary Dirichlet processes
Anthony Quinn,Miroslav Kárný +1 more
TL;DR: In this paper, the Dirichlet process prior (DPP) is used to model an unknown probability distribution, F. This eliminates the need for parametric model assumptions, providing robustness in problems where there is significant model uncertainty.
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.