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.

Autorentext

Hao Jiang received the B.Sc. degree in Mathematics from the Harbin Institute of Technology, Harbin, China, in 2009. She received the Ph.D. degree from the University of Hong Kong, in 2013. She was the recipient of the University Postgraduate Fellowships in 2010. In 2010 and 2012, she was a Visiting Scholar with Soka University, Tokyo, Japan, and Kyoto University, Kyoto, Japan, respectively. She is currently a full Professor with the School of Mathematics, Renmin University of China, Beijing, China. Her research interests include learning-based modeling in bioinformatics, optimization, and control of complex systems. She has published more than 60 refereed journal and conference papers. In addition, she was the recipient of Best paper award of ISB in 2012, and the Best paper award finalist award of DDCLS in 2022. Wai-Ki Ching is a full Professor at the Department of Mathematics, University of Hong Kong. He obtained his B. Sc. and M. Phil. in Mathematics from University of Hong Kong and his Ph.D. Systems Engineering and Engineering Management from Chinese University of Hong Kong. He received 2013 Higher Education Outstanding Scientific Research Output Awards (Second Prize) from the Ministry of Education, China (2014), Distinguished Alumni Award, Faculty of Engineering, Chinese University of Hong Kong (2017), 2019 Higher Education Outstanding Scientific Research Output Awards (Second Prize), Hunan Province, China (2019), Outstanding Research Student Supervisor Award, University of Hong Kong (2020) and he was World's Top 2% Most-cited Scientists (2021) by Stanford University. His research interests are Matrix Computations and Stochastic Modeling for Quantitative Finance and Bioinformatics. He is an author/editor of over 350 publications including over 250 journal papers, 5 edited journal special issues, 6 books and over 110 book chapters and conference proceedings.

Inhalt
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.