TY  - BOOK
AU  - Ching,Wai-Ki
AU  - Ng,Michael K.
ED  - SpringerLink (Online service)
TI  - Markov Chains: Models, Algorithms and Applications
T2  - International Series in Operations Research & Management Science,
SN  - 9780387293370
U1  - 519.2 23
PY  - 2006///
CY  - Boston, MA
PB  - Springer US
KW  - MATHEMATICS
KW  - COMPUTER SCIENCE
KW  - DISTRIBUTION (PROBABILITY THEORY)
KW  - BUSINESS LOGISTICS
KW  - PROBABILITY THEORY AND STOCHASTIC PROCESSES
KW  - OPERATIONS RESEARCH/DECISION THEORY
KW  - MATHEMATICAL MODELING AND INDUSTRIAL MATHEMATICS
KW  - PRODUCTION/LOGISTICS
KW  - PROBABILITY AND STATISTICS IN COMPUTER SCIENCE
KW  - MATH APPLICATIONS IN COMPUTER SCIENCE
N1  - Queueing Systems and the Web -- Re-manufacturing Systems -- Hidden Markov Model for Customers Classification -- Markov Decision Process for Customer Lifetime Value -- Higher-order Markov Chains -- Multivariate Markov Chains -- Hidden Markov Chains
N2  - Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order multivariate models, and higher-order hidden models. In each case, the focus is on the important kinds of applications that can be made with the class of models being considered in the current chapter. Special attention is given to numerical algorithms that can efficiently solve the models. Therefore, Markov Chains: Models, Algorithms and Applications outlines recent developments of Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and many other practical systems
UR  - http://dx.doi.org/10.1007/0-387-29337-X
ER  -