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Variational Bayesian Inference with Stochastic Search John Paisley1 David M. Blei3 Michael I. Jordan1,2 1
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Document Date: 2015-03-12 00:16:22

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City

San Diego / Edinburgh / /

Company

The New York Times / Neural Information Processing Systems / Google / J&J / The times / /

Country

Jordan / United Kingdom / Scotland / /

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Facility

University College London / /

IndustryTerm

online inference / stochastic search ˆg / stochastic search gradient / variational message passing algorithm / prediction law / stochastic search variational inference / Online learning / proposed stochastic search algorithm / typical solution / stochastic search direction / closed-form solution / stochastic algorithms / variational inference algorithms / stochastic search / local optimal solution / stochastic search methods / search algorithms / /

Organization

Gatsby Computational Neuroscience Unit / Department of EECS / University College London / National Science Foundation / American Statistical Association / Department of Computer Science / Alfred P. Sloan / UC Berkeley / Department of Statistics / Princeton University / U.S. Securities and Exchange Commission / /

Position

author / /

ProgrammingLanguage

VB / /

PublishedMedium

The New York Times / Machine Learning / Journal of Machine Learning Research / Journal of the American Statistical Association / /

Technology

existing MFVB algorithms / variational message passing algorithm / Neuroscience / stochastic algorithms / base algorithm / variational inference algorithms / slow algorithm / alternative algorithm / Machine Learning / Stochastic search algorithms / Simulation / proposed stochastic search algorithm / stochastic search VB algorithm / /

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