Preface -- 1.Introduction and First Examples -- Part I Finite Horizon Optimization Problems and Financial Markets -- 2.Theory of Finite Horizon Markov Decision Processes -- 3.The Financial Markets -- 4.Financial Optimization Problems -- Part II Partially Observable Markov Decision Problems -- 5.Partially Observable Markov Decision Processes -- 6.Partially Observable Markov Decision Problems in Finance -- Part III Infinite Horizon Optimization Problems -- 7.Theory of Infinite Horizon Markov Decision Processes -- 8.Piecewise Deterministic Markov Decision Processes -- 9.Optimization Problems in Finance and Insurance -- Part IV Stopping Problems -- 10.Theory of Optimal Stopping Problems -- 11.Stopping Problems in Finance -- Part V Appendix -- A.Tools from Analysis -- B.Tools from Probability -- C.Tools from Mathematical Finance -- References -- Index.

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. By using a structural approach many technicalities (concerning measure theory) are avoided. They cover problems with finite and infinite horizons, as well as partially observable Markov decision processes, piecewise deterministic Markov decision processes and stopping problems. The book presents Markov decision processes in action and includes various state-of-the-art applications with a particular view towards finance. It is useful for upper-level undergraduates, Master's students and researchers  in both applied probability and finance, and provides exercises (without solutions).