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Books > Business & Economics > Business & management > Management & management techniques > Operational research
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Markov Chains - Models, Algorithms and Applications (Paperback, 2nd ed. 2013)
Loot Price: R3,745
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Markov Chains - Models, Algorithms and Applications (Paperback, 2nd ed. 2013)
Series: International Series in Operations Research & Management Science, 189
Expected to ship within 18 - 22 working days
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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). Chapter 2 discusses the applications of continuous time
Markov chains to model queueing systems and discrete time Markov
chain for computing the PageRank, the ranking of websites on the
Internet. Chapter 3 studies Markovian models for manufacturing and
re-manufacturing systems and presents closed form solutions and
fast numerical algorithms for solving the captured systems. In
Chapter 4, the authors present a simple hidden Markov model (HMM)
with fast numerical algorithms for estimating the model parameters.
An application of the HMM for customer classification is also
presented. Chapter 5 discusses Markov decision processes for
customer lifetime values. Customer Lifetime Values (CLV) is an
important concept and quantity in marketing management. The authors
present an approach based on Markov decision processes for the
calculation of CLV using real data. Chapter 6 considers
higher-order Markov chain models, particularly a class of
parsimonious higher-order Markov chain models. Efficient estimation
methods for model parameters based on linear programming are
presented. Contemporary research results on applications to demand
predictions, inventory control and financial risk measurement are
also presented. In Chapter 7, a class of parsimonious multivariate
Markov models is introduced. Again, efficient estimation methods
based on linear programming are presented. Applications to demand
predictions, inventory control policy and modeling credit ratings
data are discussed. Finally, Chapter 8 re-visits hidden Markov
models, and the authors present a new class of hidden Markov models
with efficient algorithms for estimating the model parameters.
Applications to modeling interest rates, credit ratings and default
data are discussed. This book is aimed at senior undergraduate
students, postgraduate students, professionals, practitioners, and
researchers in applied mathematics, computational science,
operational research, management science and finance, who are
interested in the formulation and computation of queueing networks,
Markov chain models and related topics. Readers are expected to
have some basic knowledge of probability theory, Markov processes
and matrix theory.
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