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Statistical theory / Bayesian statistics / Statistical inference / Hidden Markov model / Importance sampling / Normal distribution / Mixture model / Bayesian inference / Joint probability distribution / Statistics / Probability and statistics / Monte Carlo methods


A New Approach to Probabilistic Programming Inference Frank Wood Department of Engineering University of Oxford
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Document Date: 2015-06-02 07:22:01


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File Size: 1,11 MB

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City

Cambridge / /

Company

Xerox / Infer.NET / Google / MH / Microsoft / The Open Group / /

Country

United Kingdom / /

Facility

Institute of Public Health / The University of British Columbia / /

IndustryTerm

non-trivial software development effort / probabilistic programming systems / e -d / e - o / function applications / rejection-sampling algorithm / likelihood function applications / inference machinery / random procedure applications / computing / transition operator / e - t / e -o / /

OperatingSystem

POSIX / /

Organization

University of British Columbia / /

Person

Nicky Best / Hubert W Lilliefors / Yura Perov / Andrew Thomas / Avi Pfeffer / Noah D Goodman / Roman Holenstein / Vikash Mansinghka / David Spiegelhalter / Frank Wood / David Wingate / George Marsaglia / Noah Goodman / Andreas Stuhlmueller / Brooks Paige / Wally Gilks / Christophe Andrieu / Thomas A Bray / Arnaud Doucet / Keith Bonawitz / Tom Minka / Daniel Tarlow / Daniel Roy / /

/

Position

single-threaded interpreter / given interpreter / interpreter / /

ProvinceOrState

British Columbia / /

PublishedMedium

Journal of the Royal Statistical Society / Journal of the American Statistical Association / /

Technology

rejection-sampling algorithm / PMCMC algorithms / simulation / html / /

URL

http /

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