Results 21 to 30 of about 23,712,988 (293)
A New Single Image Super-Resolution Method Based on the Infinite Mixture Model
IEEE Access, 2017 As a powerful nonparametric Bayesian model, the infinite mixture model has been successfully used in machine learning and computer vision. The success of the infinite mixture model owes to the capability clustering and density estimation.
Peitao Cheng +3 more
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Clustering gene expression time series data using an infinite Gaussian process mixture model. [PDF]
PLoS Computational Biology, 2018 Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present
Ian C McDowell +5 more
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Dirichlet Process Mixtures of Generalized Linear Models
J. Mach. Learn. Res., 2009 We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new method of nonparametric regression that accommodates continuous and categorical inputs, and responses that can be modeled by a generalized linear model. We prove conditions for the asymptotic unbiasedness of the DP-GLM regression mean function estimate.
Lauren Hannah +2 more
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Remote Sensing, 2021 In this paper, we propose a Dirichlet process (DP) mixture model of Gamma distributions, which is an extension of the finite Gamma mixture model to the infinite case.
Ahmed Almulihi +5 more
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BNPmix: An R Package for Bayesian Nonparametric Modeling via Pitman-Yor Mixtures
Journal of Statistical Software, 2021 BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust generalization of the popular class of Dirichlet process mixture models.
Riccardo Corradin +2 more
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Dirichlet Process Mixtures of Generalized Mallows Models
CoRR, 2012 We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first approach uses a slice sampling subcomponent for estimating cluster parameters.
Marina Meila, Harr Chen
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Graph Mining with Variational Dirichlet Process Mixture Models [PDF]
Proceedings of the 2008 SIAM International Conference on Data Mining, 2008 Graph data such as chemical compounds and XML documents are getting more common in many application domains. A main difficulty of graph data processing lies in the intrinsic high dimensionality of graphs, namely, when a graph is represented as a binary feature vector of indicators of all possible subgraph patterns, the dimensionality gets too large for
Koji Tsuda, Kenichi Kurihara
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Nonlinear Models Using Dirichlet Process Mixtures
J. Mach. Learn. Res., 2007 We introduce a new nonlinear model for classification, in which we model the joint distribution of response variable, y, and covariates, x, non-parametrically using Dirichlet process mixtures. We keep the relationship between y and x linear within each component of the mixture.
Babak Shahbaba, Radford M. Neal
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Fast Bayesian Inference in Dirichlet Process Mixture Models [PDF]
Journal of Computational and Graphical Statistics, 2011 There has been increasing interest in applying Bayesian nonparametric methods in large samples and high dimensions. As Markov chain Monte Carlo (MCMC) algorithms are often infeasible, there is a pressing need for much faster algorithms. This article proposes a fast approach for inference in Dirichlet process mixture (DPM) models.
Lianming, Wang, David B, Dunson
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Modeling and visualizing uncertainty in gene expression clusters using Dirichlet process mixtures [PDF]
, 2009 Although the use of clustering methods has rapidly become one of the standard computational approaches in the literature of microarray gene expression data, little attention has been paid to uncertainty in the results obtained. Dirichlet process mixture (
Rasmussen, Carl Edward +7 more
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