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Hierarchical Dirichlet processes
 Journal of the American Statistical Association
, 2004
"... program. The authors wish to acknowledge helpful discussions with Lancelot James and Jim Pitman and the referees for useful comments. 1 We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture comp ..."
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Cited by 927 (79 self)
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nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process
The Dynamic Hierarchical Dirichlet Process
 In Proc. of ICML’08
, 2008
"... The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are represented via a mixture associated with an appropriate underlying model, in the framework of HDP. The statistical prop ..."
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Cited by 30 (4 self)
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The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are represented via a mixture associated with an appropriate underlying model, in the framework of HDP. The statistical
Hierarchical Dirichlet Processes with . . .
"... Data sets involving multiple groups with shared characteristics frequently arise in practice. In this paper we extend hierarchical Dirichlet processes to model such data. Each group is assumed to be generated from a template mixture model with group level variability in both the mixing proportions a ..."
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Data sets involving multiple groups with shared characteristics frequently arise in practice. In this paper we extend hierarchical Dirichlet processes to model such data. Each group is assumed to be generated from a template mixture model with group level variability in both the mixing proportions
Hierarchical Dirichlet Scaling Process
"... We present the hierarchical Dirichlet scaling process (HDSP), a Bayesian nonparametric mixed membership model for multilabeled data. We construct the HDSP based on the gamma representation of the hierarchical Dirichlet process (HDP) which allows scaling the mixture components. With such construc ..."
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We present the hierarchical Dirichlet scaling process (HDSP), a Bayesian nonparametric mixed membership model for multilabeled data. We construct the HDSP based on the gamma representation of the hierarchical Dirichlet process (HDP) which allows scaling the mixture com
Hierarchical Dirichlet Processes
"... We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this se ..."
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consider a hierarchical model, specifically one in which the base measure for the child Dirichlet processes is itself distributed according to a Dirichlet process. Such a base measure being discrete, the child Dirichlet processes necessarily share atoms. Thus, as desired, the mixture models
The Dynamic Hierarchical Dirichlet Process
"... The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are represented via a mixture associated with an appropriate underlying model, in the framework of HDP. The statistical prop ..."
Abstract
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The dynamic hierarchical Dirichlet process (dHDP) is developed to model the timeevolving statistical properties of sequential data sets. The data collected at any time point are represented via a mixture associated with an appropriate underlying model, in the framework of HDP. The statistical
Nested hierarchical dirichlet processes
, 2012
"... Abstract—We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP generalizes the nested Chinese restaurant process (nCRP) to allow each word to follow its own path to a topic node according to a perdocument distribution over the paths on a shared tree. Th ..."
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Cited by 6 (3 self)
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Abstract—We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP generalizes the nested Chinese restaurant process (nCRP) to allow each word to follow its own path to a topic node according to a perdocument distribution over the paths on a shared tree
A hierarchical dirichlet language model
 Natural Language Engineering
, 1994
"... We discuss a hierarchical probabilistic model whose predictions are similar to those of the popular language modelling procedure known as 'smoothing'. A number of interesting differences from smoothing emerge. The insights gained from a probabilistic view of this problem point towards new ..."
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Cited by 95 (3 self)
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We discuss a hierarchical probabilistic model whose predictions are similar to those of the popular language modelling procedure known as 'smoothing'. A number of interesting differences from smoothing emerge. The insights gained from a probabilistic view of this problem point towards new
Hierarchical Dirichlet Trees for Information Retrieval
"... We propose a principled probabilisitc framework which uses trees over the vocabulary to capture similarities among terms in an information retrieval setting. This allows the retrieval of documents based not just on occurrences of specific query terms, but also on similarities between terms (an effec ..."
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Cited by 1 (0 self)
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effect similar to query expansion). Additionally our principled generative model exhibits an effect similar to inverse document frequency. We give encouraging experimental evidence of the superiority of the hierarchical Dirichlet tree compared to standard baselines. 1
Hierarchical Dirichlet model for document classification
 In ICML 2005
, 2005
"... The proliferation of text documents on the web as well as within institutions necessitates their convenient organization to enable efficient retrieval of information. Although text corpora are frequently organized into concept hierarchies or taxonomies, the classification of the documents into the h ..."
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Cited by 12 (2 self)
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into the hierarchy is expensive in terms human effort. We present a novel and simple hierarchical Dirichlet generative model for text corpora and derive an efficient algorithm for the estimation of model parameters and the unsupervised classification of text documents into a given hierarchy. The class conditional
Results 1  10
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13,344