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Markov models / Markov chain Monte Carlo / Computational statistics / Markov processes / Markov chain / Metropolis–Hastings algorithm / Rejection sampling / Mixture model / Random walk / Statistics / Probability and statistics / Monte Carlo methods


Distributed and Adaptive Darting Monte Carlo through Regenerations Sungjin Ahn Department of Computer Science University of California, Irvine Irvine, CA, USA
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Document Date: 2013-04-11 10:50:54

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File Size: 1,47 MB

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Sampling / /

Company

Neural Information Processing Systems / IEEE Journal / /

Country

Jordan / United States / /

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Facility

Max Welling Informatics Institute University of Amsterdam Amsterdam / University of Washington Department / Computer Science University of California / /

IndustryTerm

sensor networks / genetic algorithm / regeneration algorithm / adaptive algorithm / mode search / easy bayesian computing / preliminary mode search / Wireless Sensor Network / site/kenichikurihara/academic-software/ variational-dirichlet-process-gaussianmixture-model / online proceedings / important tool / /

Organization

Regenerations Sungjin Ahn Department / American Statistical Association / Computer Science University / University of Amsterdam Amsterdam / University of California / Irvine / University of Washington Department of Statistics / /

Person

F. R. Hojen-Sorensen / Stuart J. Russell / Max Welling / /

ProgrammingLanguage

R / /

ProvinceOrState

California / /

PublishedMedium

Machine Learning / Journal of the American Statistical Association / /

Technology

regeneration algorithm / population MCMC algorithm / one processor / proposed algorithm / artificial intelligence / 1 Algorithm / DMC algorithm / genetic algorithm / machine learning / simulation / adaptive algorithm / following algorithms / darting Monte Carlo algorithm / population-based MCMC algorithm / MCMC algorithms / Darting algorithm / /

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