Monte Carlo method

Results: 631



#Item
51Cranking: Combining Rankings Using Conditional Probability Models on Permutations Guy Lebanon John Lafferty School of Computer Science, Carnegie Mellon University, Pittsburgh, PAUSA

Cranking: Combining Rankings Using Conditional Probability Models on Permutations Guy Lebanon John Lafferty School of Computer Science, Carnegie Mellon University, Pittsburgh, PAUSA

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Source URL: www.cc.gatech.edu

Language: English - Date: 2009-09-04 13:43:12
52Cryogen Spray Cooling for Laser Dermatology: Powerful Strategy for Thermal Protection of Epidermis and Laser Power Enhancement Bin CHEN State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong Unive

Cryogen Spray Cooling for Laser Dermatology: Powerful Strategy for Thermal Protection of Epidermis and Laser Power Enhancement Bin CHEN State Key Laboratory of Multiphase Flow in Power Engineering, Xi’an Jiaotong Unive

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Source URL: www.icmmr.org

Language: English
53Cognition–300  Contents lists available at SciVerse ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT

Cognition–300 Contents lists available at SciVerse ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT

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Source URL: cocosci.berkeley.edu

Language: English - Date: 2015-10-08 17:16:09
54Monte Carlo simulation in MS Excel The Monte Carlo method is based on the generation of multiple trials to determine the expected value of a random variable. The basis of the method is provided by the following relations

Monte Carlo simulation in MS Excel The Monte Carlo method is based on the generation of multiple trials to determine the expected value of a random variable. The basis of the method is provided by the following relations

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Source URL: cdn.projectsmart.co.uk

Language: English - Date: 2015-07-26 12:27:46
    55Online Evolution for Multi-Action Adversarial Games Niels Justesen1 , Tobias Mahlmann2 , and Julian Togelius3 1 2  IT University of Copenhagen

    Online Evolution for Multi-Action Adversarial Games Niels Justesen1 , Tobias Mahlmann2 , and Julian Togelius3 1 2 IT University of Copenhagen

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    Source URL: julian.togelius.com

    Language: English - Date: 2016-03-20 21:44:35
    56Markov chain Monte Carlo for continuous-time discrete-state systems Vinayak A. P. Rao Gatsby Computational Neuroscience Unit

    Markov chain Monte Carlo for continuous-time discrete-state systems Vinayak A. P. Rao Gatsby Computational Neuroscience Unit

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    Source URL: www.stat.purdue.edu

    Language: English - Date: 2014-08-30 13:43:20
    57PHYSICAL REVIEW E 78, 046704 !2008

    PHYSICAL REVIEW E 78, 046704 !2008" Variational method for estimating the rate of convergence of Markov-chain Monte Carlo algorithms Fergal P. Casey* Complex and Adaptive Systems Laboratory, University College Dublin, D

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    Source URL: cbsu.tc.cornell.edu

    Language: English - Date: 2014-10-31 16:45:19
      58Monte Carlo-based method to estimate the capacity value of wind power considering operational aspects

      Monte Carlo-based method to estimate the capacity value of wind power considering operational aspects

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      Source URL: energyexemplar.com

      Language: English - Date: 2015-06-15 10:05:49
        59Random NumbersUsing PRG .......

        Random NumbersUsing PRG .......

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        Source URL: genome.sph.umich.edu

        Language: English - Date: 2012-10-31 21:30:08
        601  The Swendsen–Wang method Several enhancements of Monte Carlo methods are based on a remarkable trick: take a big and difficult problem, and replace it by an even bigger problem that contains the

        1 The Swendsen–Wang method Several enhancements of Monte Carlo methods are based on a remarkable trick: take a big and difficult problem, and replace it by an even bigger problem that contains the

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        Source URL: www.inference.phy.cam.ac.uk

        Language: English - Date: 2006-04-13 14:58:31