Markov Decision Processes with Applications to Finance 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 Masters students and researchers in both applied probability and finance and provides exercises (without solutions). .