The dynamic hierarchical Dirichlet process

Lu Ren; David B. Dunson; Lawrence Carin

doi:10.1145/1390156.1390260

The dynamic hierarchical Dirichlet process

Lu Ren, David B. Dunson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

99 Scopus citations

Abstract

The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving 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 properties of data collected at consecutive time points are linked via a random parameter that controls their probabilistic similarity. The sharing mechanisms of the time-evolving data are derived, and a relatively simple Markov Chain Monte Carlo sampler is developed. Experimental results are presented to demonstrate the model. Copyright 2008 by the author(s)/owner(s).

Original language	English (US)
Title of host publication	Proceedings of the 25th International Conference on Machine Learning
Publisher	Association for Computing Machinery (ACM)
Pages	824-831
Number of pages	8
ISBN (Print)	9781605582054
DOIs	https://doi.org/10.1145/1390156.1390260
State	Published - Jan 1 2008
Externally published	Yes

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10.1145/1390156.1390260

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title = "The dynamic hierarchical Dirichlet process",

abstract = "The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving 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 properties of data collected at consecutive time points are linked via a random parameter that controls their probabilistic similarity. The sharing mechanisms of the time-evolving data are derived, and a relatively simple Markov Chain Monte Carlo sampler is developed. Experimental results are presented to demonstrate the model. Copyright 2008 by the author(s)/owner(s).",

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N2 - The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving 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 properties of data collected at consecutive time points are linked via a random parameter that controls their probabilistic similarity. The sharing mechanisms of the time-evolving data are derived, and a relatively simple Markov Chain Monte Carlo sampler is developed. Experimental results are presented to demonstrate the model. Copyright 2008 by the author(s)/owner(s).

AB - The dynamic hierarchical Dirichlet process (dHDP) is developed to model the time-evolving 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 properties of data collected at consecutive time points are linked via a random parameter that controls their probabilistic similarity. The sharing mechanisms of the time-evolving data are derived, and a relatively simple Markov Chain Monte Carlo sampler is developed. Experimental results are presented to demonstrate the model. Copyright 2008 by the author(s)/owner(s).

UR - http://portal.acm.org/citation.cfm?doid=1390156.1390260

UR - http://www.scopus.com/inward/record.url?scp=56449123630&partnerID=8YFLogxK

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M3 - Conference contribution

SN - 9781605582054

SP - 824

EP - 831

BT - Proceedings of the 25th International Conference on Machine Learning

PB - Association for Computing Machinery (ACM)

ER -

The dynamic hierarchical Dirichlet process

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