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Statistical inference / Monte Carlo methods / Bayes estimator / Bayesian inference / Estimator / Metropolis–Hastings algorithm / Importance sampling / Bias of an estimator / Loss function / Statistics / Estimation theory / Statistical theory


Loss-calibrated Monte Carlo Action Selection Ehsan Abbasnejad Justin Domke NICTA & ANU
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Document Date: 2014-11-21 10:16:12

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City

San Francisco / New York / /

Company

John Wiley & Sons Inc. / Neural Information Processing Systems / Morgan Kaufmann Publishers Inc. / /

Country

Jordan / United States / /

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Facility

plant Control We / University of Toronto / /

IndustryTerm

non-negative utilities / estimated utilities / expected utilities / random walk metropolis algorithms / real-time applications / large chain / dimensional utilities / online action selection / action utilities / /

Organization

American Statistical Association / ICT Centre of Excellence / Artificial Copyright Intelligence / Australian Government / Association for the Advancement / European Union / Australian Research Council / Department of Broadband / Communications and the Digital Economy / University of Toronto / /

Person

Scott Sanner / Ehsan Abbasnejad Justin Domke / /

Product

Archos TV+ Portable Video Player (PVP) / /

PublishedMedium

Machine Learning / Journal of the American Statistical Association / /

Technology

Broadband / artificial intelligence / Machine Learning / Simulation / random walk metropolis algorithms / /

URL

www.aaai.org / /

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