Paperback. This textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments - with indications as to why a particular result holds, and also how it is connected with other results - and illustrated by examples. Wherever possible, the book includes references to more specialized texts containing both proofs and more advanced material related to the topics covered. Developed from the lecture notes for a course on stochastic modelling, this textbook reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Bibliographic Details

Title: Elements Of Stochastic Modelling (Paperback)
Publisher: World Scientific Publishing Co Pte Ltd, Singapore
Publication Date: 2003
Language: English
ISBN 10: 9812383018
ISBN 13: 9789812383013
Binding: Paperback
Condition: new

About this title

Synopsis

This textbook has been developed from the lecture notes for a one-semester course on stochastic modelling. It reviews the basics of probability theory and then covers the following topics: Markov chains, Markov decision processes, jump Markov processes, elements of queueing theory, basic renewal theory, elements of time series and simulation. Rigorous proofs are often replaced with sketches of arguments - with indications as to why a particular result holds, and also how it is connected with other results - and illustrated by examples. Wherever possible, the book includes references to more specialised texts containing both proofs and more advanced material related to the topics covered.

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Readership: Upper level undergraduates, graduate students, lecturers and researchers in probability theory and stochastic processes

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