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[null,"1.","Record Nr.",null,"UNINA9910830223203321"]
[null,null,"Autore",null,"Chakravarthy Srinivas R."]
[null,null,"Titolo",null,"Introduction to matrix-analytic methods in queues . 2 Analytical and simulation approach - queues and simulation / / Srinivas R. Chakravarthy"]
[null,null,"Pubbl/distr/stampa",null,"Hoboken, NJ : , : John Wiley & Sons, Inc., , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"1-394-17420-9"]
[{"$nulls":4},"1-394-17418-7"]
[null,null,"Descrizione fisica",null,"1 online resource (442 pages)"]
[null,null,"Disciplina",null,"519.82"]
[null,null,"Soggetti",null,"Matrix analytic methods"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Front Matter -- Single-Server Queues Embedded at Departure Epochs -- Single-Server Queues Embedded at Arrival Epochs -- Single-Server Queues Based on Arbitrary Epochs -- Busy Period in Queues -- Multi-Server Queues -- Finite-Capacity Queues -- Simulation -- References -- Index -- Summary of Volume 1 -- Other titles from ISTE in Mathematics and Statistics."]
[null,null,null,"Sommario/riassunto","Matrix-analytic methods (MAM) were introduced by Professor Marcel Neuts and have been applied to a variety of stochastic models since. In order to provide a clear and deep understanding of MAM while showing their power, this book presents MAM concepts and explains the results using a number of worked-out examples. This book's approach will inform and kindle the interest of researchers attracted to this fertile field. To allow readers to practice and gain experience in the algorithmic and computational procedures of MAM, Introduction to Matrix-Analytic Methods in Queues 2 provides a number of computational exercises. It also incorporates simulation as another tool for studying complex stochastic models, especially when the state space of the underlying stochastic models under analytic study grows exponentially. This book's detailed approach will make it more accessible for readers interested in learning about MAM in stochastic models."]