Titlebook: Markov Processes and Controlled Markov Chains; Zhenting Hou,Jerzy A. Filar,Anyue Chen Book 20021st edition Kluwer Academic Publishers 2002

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Book 20021st editionury. In parallel, the theory of controlled Markov chains (or Markov decision processes) was being pioneered by control engineers and operations researchers. Researchers in Markov processes and controlled Markov chains have been, for a long time, aware of the synergies between these two subject areas

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last century. In parallel, the theory of controlled Markov chains (or Markov decision processes) was being pioneered by control engineers and operations researchers. Researchers in Markov processes and controlled Markov chains have been, for a long time, aware of the synergies between these two sub

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Piecewise Deterministic Markov Processes and Semi-Dynamic Systemsivor functions so that they are absolutely continuous with respect to the time . Furthermore, the state jump measure of a PDMP is introduced. When accompanied by the (state) transition kernal, it plays the same role to PDMPs as Q-matix to Q-processes.

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Linear Program for Communicating MDPs with Multiple Constraintsthe unichain linear program solves the average reward communicating MDPs with multiple constraints on average expected costs, but also to demonstrate that the optimal gain for the communicating MDPs with multiple constraints on average expected costs is constant.

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Markov Skeleton Processesoption pricing models, as particular cases. The present paper aims to fully expound the background and the history source of the introduction of Markov skeleton processes, and we deduce the forward and backward equation and use them as a powerful tool to obtain the criteria of regularity.

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Controlled Markov Chains with Utility Functionsctions. We show that the utility problem with a general policy is equivalent to a terminal problem with a Markov policy on the augmented state space. Finally it is shown that the utility problem has an optimal policy in the class of general policies on the original state space.