Markov chains: models, algorithms and applications
Ching, W. K.
Markov chains: models, algorithms and applications Ching, W. K. - 2nd ed. - New York Springer 2013 - xvi, 243 p. - International series in operations research & management science, Vol. 189 .
This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data.
This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs).
9781461463115
Markov processes
Economics
Distribution (Probability theory)
Operations research
658.40301 / C4M2
Markov chains: models, algorithms and applications Ching, W. K. - 2nd ed. - New York Springer 2013 - xvi, 243 p. - International series in operations research & management science, Vol. 189 .
This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data.
This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods for solving linear systems will be introduced for finding the stationary distribution of a Markov chain. The chapter then covers the basic theories and algorithms for hidden Markov models (HMMs) and Markov decision processes (MDPs).
9781461463115
Markov processes
Economics
Distribution (Probability theory)
Operations research
658.40301 / C4M2