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Applied mathematics / Computational statistics / Markov models / Markov chain Monte Carlo / Estimation theory / Particle filter / Metropolis–Hastings algorithm / Parallel tempering / Markov chain / Statistics / Monte Carlo methods / Probability and statistics


Efficient Hierarchical MCMC for Policy Search Malcolm Strens [removed] Room G020, A9 Building, QinetiQ, Ively Road, Farnborough, Hampshire, GU14 0LX, UK.
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Document Date: 2008-12-01 11:20:41

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File Size: 318,37 KB

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City

San Francisco / Banff / /

Company

Neural Information Processing Systems / MIT Press / QinetiQ Ltd. / Monte Carlo / /

Country

Jordan / Canada / United Kingdom / /

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Facility

A9 Building / University of Toronto / /

IndustryTerm

hierarchical algorithm / policy search task / simulated annealing solutions / policy search problem / policy search / genuine applications / signal processing / conventional control law / sub-optimal solution / policy search method / policy search optimization task / scientific computing / energy / /

MarketIndex

set 1000 / /

Organization

American Statistical Association / MIT / UK Ministry of Defence Corporate Research Programme / University of Toronto / /

Person

Drew Bagnell / Graham Watson / Nick Everett / Simon Maskell / Mansour / Morgan Kaufmann / Malcolm Strens / /

Position

Teller / neural network controller / controller / optimized controller / /

PublishedMedium

Machine Learning / Journal of Chemical Physics / Journal of the American Statistical Association / /

Technology

end Algorithm / hierarchical algorithm / resulting algorithm / neural network / artificial intelligence / caching / machine learning / simulation / Hamiltonian MCMC algorithm / HINTS algorithm / 0 end end Algorithm / /

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