This paperconsidersthe problem of representingcomplex systems that evolve stochastically over time. Dynamic Bayesian networks provide a compact representation for stochastic proce...
Solving linear systems with a large number of variables is at the core of many scienti c problems. Parallel processing techniques for solving such systems have received much attent...
Arun Nagari, Itamar Elhanany, Ben Thompson, Fangxi...
Markov decision processes (MDPs) are controllable discrete event systems with stochastic transitions. The payoff received by the controller can be evaluated in different ways, dep...
Markov decision processes (MDPs) are controllable discrete event systems with stochastic transitions. Performances of an MDP are evaluated by a payoff function. The controller of ...
Forecasting future events based on historic data is useful in many domains like system management, adaptive query processing, environmental monitoring, and financial planning. We...