Markov Chains: Theory and Applications
Series: Handbook of Statistics; 52;
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Product details:
- Publisher Academic Press
- Date of Publication 16 June 2025
- ISBN 9780443295768
- Binding Hardback
- No. of pages420 pages
- Size 229x152 mm
- Language English 700
Categories
Long description:
Markov Chains: Theory and Applications, Volume 52 in the Handbook of Statistics series, highlights new advances in the field, with this new volume presenting interesting chapters on topics such as Markov Chain Estimation, Approximation, and Aggregation for Average Reward Markov Decision Processes and Reinforcement Learning, Ladder processes: symmetric functions and semigroups, Continuous-time Markov Chains and Models: Study via Forward Kolmogorov System, Analysis of Data Following Finite-State Continuous-Time Markov Chains, Computational applications of poverty measurement through Markov model for income classes, and more.
Other sections cover Estimation and calibration of continuous time Markov chains, Additive High-Order Markov Chains, The role of the random-product technique in the theory of Markov chains on a countable state space., On estimation problems based on type I Longla copulas, and Long time behavior of continuous time Markov chains.
- Provides the latest information on Markov Chains: Theory And Applications
- Offers outstanding and original reviews on a range of Markov Chains research topics
- Serves as an indispensable reference for researchers and students alike
Table of Contents:
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. Esquível
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
