Markov Chains: Theory and Applications

Preface
Arni S.R. Srinivasa Rao
1. Markov Chain Estimation, Approximation, and Aggregation for Average Reward Markov Decision Processes and Reinforcement Learning
Ronald Ortner
2. Ladder processes: symmetric functions and semigroups
Philip Feinsilver
3. Continuous-time Markov Chains and Models: Study via Forward Kolmogorov System
Alexander Zeifman
4. Analysis of Data Following Finite-State Continuous-Time Markov Chains
Wenyaw Chan
5. Computational applications of poverty measurement through Markov model for income classes
Guglielmo D'Amico
6. Estimation and calibration of continuous time Markov chains
Manuel L. Esquivel
7. Additive High-Order Markov Chains
Serhii Melnyk, Galyna Prytula and Oleg Victorovich Usatenko
8. The role of the random-product technique in the theory of Markov chains on a countable state space.
Brian Fralix, Amin Khademi and Farhad Hasankhani
9. On estimation problems based on type I Longla copulas
Martial Longla
10. Long time behaviour of continuous time Markov chains
Xueping Huang
11. To be Determined
Alan Krinik

Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.

• Estocástica

Markov decision process; Markov reward process; Markov chain perturbation; Markov chain approximation; MDP aggregation; reinforcement learning