About: Hierarchical Dirichlet process

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In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a Dirichlet process for each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process. This method allows groups to share statistical strength via sharing of clusters across groups. The base distribution being drawn from a Dirichlet process is important, because draws from a Dirichlet process are atomic probability measures, and the atoms will appear in all group-level Dirichlet processes. Since each atom corresponds to a cluster, clusters are shared across all groups. It was developed by Yee Whye Teh, Michael I. Jordan, and David Blei and published in the Journal o

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rdf:type	yago:WikicatNon-parametricBayesianMethods yago:WikicatStochasticProcesses yago:Ability105616246 yago:Abstraction100002137 yago:Cognition100023271 yago:Concept105835747 yago:Content105809192 yago:Hypothesis105888929 yago:Idea105833840 yago:Know-how105616786 yago:Method105660268 yago:Model105890249 yago:PsychologicalFeature100023100 programming language yago:StochasticProcess113561896
rdfs:label	Hierarchical Dirichlet process (en)
rdfs:comment	In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a Dirichlet process for each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process. This method allows groups to share statistical strength via sharing of clusters across groups. The base distribution being drawn from a Dirichlet process is important, because draws from a Dirichlet process are atomic probability measures, and the atoms will appear in all group-level Dirichlet processes. Since each atom corresponds to a cluster, clusters are shared across all groups. It was developed by Yee Whye Teh, Michael I. Jordan, and David Blei and published in the Journal o (en)
dcterms:subject	Nonparametric Bayesian statistics Stochastic processes
Wikipage page ID	34292221 (xsd:integer)
Wikipage revision ID	1100589533 (xsd:integer)
Link from a Wikipage to another Wikipage	Bayesian probability David Blei Pitman-Yor process Machine learning Nonparametric Bayesian statistics Statistics Chinese Restaurant Process Gamma process Latent Dirichlet allocation Dirichlet process Journal of the American Statistical Association Stochastic processes Hidden Markov model Grouped data Michael I. Jordan Non-parametric statistics Topic model Yee Whye Teh dbr:Hierarchical_Pitman-Yor_process dbr:Infinite_hidden_Markov_model dbr:Matthew_J._Beal dbr:Sequence_memoizer
sameAs	Hierarchical Dirichlet process Hierarchical Dirichlet process Hierarchical Dirichlet process Hierarchical Dirichlet process Hierarchical Dirichlet process
dbp:wikiPageUsesTemplate	dbt:More_citations_needed dbt:Multiple_issues dbt:Primary_sources dbt:Scholia
has abstract	In statistics and machine learning, the hierarchical Dirichlet process (HDP) is a nonparametric Bayesian approach to clustering grouped data. It uses a Dirichlet process for each group of data, with the Dirichlet processes for all groups sharing a base distribution which is itself drawn from a Dirichlet process. This method allows groups to share statistical strength via sharing of clusters across groups. The base distribution being drawn from a Dirichlet process is important, because draws from a Dirichlet process are atomic probability measures, and the atoms will appear in all group-level Dirichlet processes. Since each atom corresponds to a cluster, clusters are shared across all groups. It was developed by Yee Whye Teh, Michael I. Jordan, and David Blei and published in the Journal of the American Statistical Association in 2006, as a formalization and generalization of the published in 2002. (en)
gold:hypernym	Approach
prov:wasDerivedFrom	wikipedia-en:Hierarchical_Dirichlet_process?oldid=1100589533&ns=0
page length (characters) of wiki page	8517 (xsd:nonNegativeInteger)
foaf:isPrimaryTopicOf	wikipedia-en:Hierarchical_Dirichlet_process
is Link from a Wikipage to another Wikipage of	Latent Dirichlet allocation Dirichlet process List of RNA-Seq bioinformatics tools List of things named after Peter Gustav Lejeune Dirichlet HDP Time-series segmentation Topic model Types of artificial neural networks Yee Whye Teh
is Wikipage disambiguates of	HDP
is known for of	Yee Whye Teh
is known for of	Yee Whye Teh
is foaf:primaryTopic of	wikipedia-en:Hierarchical_Dirichlet_process

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