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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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Dissertation
Multi-task learning with Gaussian processes
TL;DR: A multi-task Gaussian process regression model that learns related functions by inducing correlations between tasks directly is considered, using this model as a reference for three other multi- task models, to provide a broad unifying view of multi- Task learning.
Proceedings Article
Supervised Dimension Reduction Using Bayesian Mixture Modeling
TL;DR: A Bayesian framework for supervised dimension reduction using a flexible nonparametric Bayesian mixture modeling approach that allows for natural clustering for the data in terms of both the response and predictor variables is developed.
Journal ArticleDOI
Multivariate spatial nonparametric modelling via kernel processes mixing.
TL;DR: A nonparametric multivariate spatial model that avoids specifying a Gaussian distribution for spatial random effects and is the first to allow both the probabilities and the point mass values of the SB prior to depend on space.
Journal ArticleDOI
Prediction of U.S. Cancer Mortality Counts Using Semiparametric Bayesian Techniques
Kaushik Ghosh,Ram C. Tiwari +1 more
TL;DR: In this article, the authors presented two models for the short-term prediction of the number of deaths arising from common cancers in the United States, which can be used to obtain the predictive distributions of the future number of death, as well their means and variances through Markov chain Monte Carlo techniques.
Proceedings ArticleDOI
Nested iGMM recognition and multiple hypothesis tracking of moving sound sources for mobile robot audition
TL;DR: Nested infinite Gaussian mixture model (iGMM) for recognizing frame based feature vectors and the multiple hypothesis tracking module provides time-series of separated audio stream using localized directions and recognition results at each frame.
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.