000 05209nam a22005295i 4500
001 978-1-4614-6312-2
003 DE-He213
005 20140220082824.0
007 cr nn 008mamaa
008 130327s2013 xxu| s |||| 0|eng d
020 _a9781461463122
_9978-1-4614-6312-2
024 7 _a10.1007/978-1-4614-6312-2
_2doi
050 4 _aHD30.23
072 7 _aKJT
_2bicssc
072 7 _aKJMD
_2bicssc
072 7 _aBUS049000
_2bisacsh
082 0 4 _a658.40301
_223
100 1 _aChing, Wai-Ki.
_eauthor.
245 1 0 _aMarkov Chains
_h[electronic resource] :
_bModels, Algorithms and Applications /
_cby Wai-Ki Ching, Ximin Huang, Michael K. Ng, Tak-Kuen Siu.
250 _a2nd ed. 2013.
264 1 _aBoston, MA :
_bSpringer US :
_bImprint: Springer,
_c2013.
300 _aXVI, 243 p. 31 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInternational Series in Operations Research & Management Science,
_x0884-8289 ;
_v189
505 0 _aIntroduction -- Manufacturing and Re-manufacturing Systems -- A Hidden Markov Model for Customer Classification -- Markov Decision Processes for Customer Lifetime Value -- Higher-order Markov Chains -- Multivariate Markov Chains -- Hidden Markov Chains.
520 _aThis 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.
650 0 _aEconomics.
650 0 _aDistribution (Probability theory).
650 0 _aOperations research.
650 1 4 _aEconomics/Management Science.
650 2 4 _aOperation Research/Decision Theory.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aProbability Theory and Stochastic Processes.
700 1 _aHuang, Ximin.
_eauthor.
700 1 _aNg, Michael K.
_eauthor.
700 1 _aSiu, Tak-Kuen.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461463115
830 0 _aInternational Series in Operations Research & Management Science,
_x0884-8289 ;
_v189
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-6312-2
912 _aZDB-2-SBE
999 _c95639
_d95639