Markov Chains: Theory and Applications (eBook, ePUB)
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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...
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
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