The theory of markov decision processes focuses on controlled markov chains in discrete time the authors establish the theory for general state and action spaces and at the same time show its application by means of numerous examples mostly taken from the fields of finance and operations research. Universitext is a series of textbooks that presents material from a wide variety markov decision processes with applications to finance institute for stochastics karlsruhe institute of technology 76128 karlsruhe tic markov decision processes are discussed and we give recent applications. Markov decision processes with applications to finance mdps with finite time horizon markov decision processes mdps motivation let xn be a markov process in discrete time with i state space e i transition kernel qn x let xn be a controlled markov process with i state space e action space a i admissible state action pairs dn e xa i transition kernel qn xa. The theory of markov decision processes focuses on controlled markov chains in discrete time the authors establish the theory for general state and action spaces illustrating its application read more. This paper deals with the risk probability for finite horizon semi markov decision processes with loss rates the criterion to be minimized is the risk probability that the total loss incurred during a finite horizon exceed a loss level for such an optimality problem we first establish the optimality equation and prove that the optimal value function is a unique solution to the optimality
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