Markov processes

Results: 1286



#Item
961Monte Carlo Bayesian Reinforcement Learning  Yi Wang WANGY @ COMP. NUS . EDU . SG Kok Sung Won KOKSUNG @ COMP. NUS . EDU . SG

Monte Carlo Bayesian Reinforcement Learning Yi Wang WANGY @ COMP. NUS . EDU . SG Kok Sung Won KOKSUNG @ COMP. NUS . EDU . SG

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Source URL: icml.cc

Language: English - Date: 2012-06-07 13:20:16
962Predicting Preference Flips in Commerce Search  Samuel Ieong Nina Mishra Microsoft Research, Mountain View, CA[removed]Or Sheffet

Predicting Preference Flips in Commerce Search Samuel Ieong Nina Mishra Microsoft Research, Mountain View, CA[removed]Or Sheffet

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Source URL: icml.cc

Language: English - Date: 2012-06-07 13:20:54
963State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction Jouni Hartikainen [removed] Aalto University, Dept. of Biomedical Engineering and Computational Sci

State-Space Inference for Non-Linear Latent Force Models with Application to Satellite Orbit Prediction Jouni Hartikainen [removed] Aalto University, Dept. of Biomedical Engineering and Computational Sci

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Source URL: icml.cc

Language: English - Date: 2012-06-07 13:20:08
964Continuous Inverse Optimal Control with Locally Optimal Examples  Sergey Levine Vladlen Koltun Computer Science Department, Stanford University, Stanford, CA[removed]USA

Continuous Inverse Optimal Control with Locally Optimal Examples Sergey Levine Vladlen Koltun Computer Science Department, Stanford University, Stanford, CA[removed]USA

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Source URL: icml.cc

Language: English - Date: 2012-06-19 08:15:16
965Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds Marek Petrik IBM T.J. Watson Research Center, Yorktown, NY, USA

Approximate Dynamic Programming By Minimizing Distributionally Robust Bounds Marek Petrik IBM T.J. Watson Research Center, Yorktown, NY, USA

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Source URL: icml.cc

Language: English - Date: 2012-06-07 13:19:56
966Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management

Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management

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Source URL: www.stat.tamu.edu

Language: English - Date: 2008-09-08 23:55:14
967Persistent random walks, variable length Markov chains and piecewise deterministic Markov processes Peggy 1

Persistent random walks, variable length Markov chains and piecewise deterministic Markov processes Peggy 1

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Source URL: math.u-bourgogne.fr

Language: English - Date: 2012-09-28 05:08:59
968Statistics of transitions for Markov chains with periodic forcing.∗ S. Herrmann and D. Landon Institut de Mathématiques de Bourgogne UMR CNRS 5584, Université de Bourgogne,

Statistics of transitions for Markov chains with periodic forcing.∗ S. Herrmann and D. Landon Institut de Mathématiques de Bourgogne UMR CNRS 5584, Université de Bourgogne,

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Source URL: math.u-bourgogne.fr

Language: English - Date: 2013-04-04 08:15:11
969The	
  exponenEal	
  mechanism	
  via	
  MCMC	
   Aleksandar	
  Makelov,	
  mentored	
  by	
  prof.	
  Salil	
  Vadhan	
   Harvard	
  College	
  ’15,	
  supported	
  by	
  HCRP	
   Mo#va#on	
    Mark

The  exponenEal  mechanism  via  MCMC   Aleksandar  Makelov,  mentored  by  prof.  Salil  Vadhan   Harvard  College  ’15,  supported  by  HCRP   Mo#va#on   Mark

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Source URL: privacytools.seas.harvard.edu

Language: English - Date: 2014-01-21 14:41:55
970arXiv:submit[removed]q-bio.NC] 11 Feb[removed]Stochastic Representations of Ion Channel Kinetics and Exact Stochastic Simulation of Neuronal Dynamics David F. Anderson1 , Bard Ermentrout2 , and Peter J. Thomas3 1

arXiv:submit[removed]q-bio.NC] 11 Feb[removed]Stochastic Representations of Ion Channel Kinetics and Exact Stochastic Simulation of Neuronal Dynamics David F. Anderson1 , Bard Ermentrout2 , and Peter J. Thomas3 1

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Source URL: www.math.wisc.edu

Language: English - Date: 2014-02-11 13:09:00