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Stochastic control
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161	Optimization and Control Richard Weber, Michaelmas Term 2014 Contents Schedules Add to Reading List Source URL: www.statslab.cam.ac.uk Language: English - Date: 2014-11-29 06:04:45 Mathematical optimization Dynamic programming Optimal control Stochastic control Bellman equation Markov decision process Kalman filter Optimization problem Secretary problem Control theory Statistics Systems theory
162	1 Series Expansion Approximations of Brownian Motion for Non-Linear Kalman Filtering of Diffusion Processes Simon Lyons, Simo S¨arkk¨a, Senior Member, IEEE, and Amos Storkey Add to Reading List Source URL: homepages.inf.ed.ac.uk Language: English - Date: 2013-11-26 10:22:33 Estimation theory Robot control Control theory Unscented transform Extended Kalman filter Kalman filter Nonlinear filter Particle filter Gaussian filter Statistics Stochastic processes Linear filters
163	The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty Ron Alterovitz Thierry Sim´eon Add to Reading List Source URL: www.ieor.berkeley.edu Language: English - Date: 2007-06-02 12:59:23 Physics Motion planning Theoretical computer science Robotics Probabilistic roadmap Stochastic roadmap simulation Holonomic Stochastic Markov chain Robot control Mathematics Statistics
164	PDF Document Add to Reading List Source URL: www.veritasdvm.com Language: English Optimal control Stochastic control English phonology Latin alphabets Control theory
165	Volatile decision dynamics: experiments, stochastic description, intermittency control and traffic optimization 1 ¨ Dirk Helbing1,2,3 , Martin Schonhof Add to Reading List Source URL: www.ima.umn.edu Language: English - Date: 2003-11-11 16:08:50
166	4\|FV0OMMN@S j~rxp{ nt{up,t \tp,.\|t}. Y}}/p{ it~,. OMMN r stv Add to Reading List Source URL: www.swd.gov.hk. Language: English - Date: 2011-01-07 15:49:49 Control theory Optimal control Stochastic control
167	Journal of Artificial Intelligence Research337 Submitted 06/08; publishedMonte Carlo Sampling Methods for Approximating Interactive POMDPs Add to Reading List Source URL: jair.org Language: English - Date: 2009-03-24 14:18:15 Probability and statistics Estimation theory Dynamic programming Markov processes Stochastic control Partially observable Markov decision process Particle filter Importance sampling Kalman filter Statistics Control theory Robot control
168	What Makes Some POMDP Problems Easy to Approximate? David Hsu∗ Wee Sun Lee∗ Add to Reading List Source URL: www.comp.nus.edu.sg Language: English - Date: 2007-10-02 20:38:57 Stochastic control Control theory Partially observable Markov decision process Markov decision process Statistics Dynamic programming Markov processes
169	DOMINANCE THEOREMS AND ERGODIC PROPERTIES OF POLLING SYSTEMS1 S. G. Foss and N. I. Chernova We consider a class of polling systems with stationary ergodic input flow such that the control in a system obeys a certain rege Add to Reading List Source URL: math.nsc.ru Language: English - Date: 2005-09-16 03:23:50 Calculus Continuous function Markov chain Ergodic theory Mathematics Control theory Itō diffusion Μ operator Mathematical analysis Markov models Stochastic differential equations
170	Genetic Programming as Policy Search in Markov Decision Processes Chris Gearhart P.O. BoxStanford, CA 94309 Add to Reading List Source URL: www.genetic-programming.org Language: English - Date: 2006-11-17 21:29:08 Probability and statistics Markov processes Stochastic control Markov models Mathematical optimization Reinforcement learning Markov decision process Automated planning and scheduling Bellman equation Statistics Mathematics Dynamic programming

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