Titlebook: Applied Probability and Stochastic Processes; Richard M. Feldman,Ciriaco Valdez-Flores Book 2010Latest edition Springer-Verlag Berlin Heid

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Markov Processes,continuous analog to a chain, namely, the Markov property is maintained and time is measured continuously. For example, suppose we are keeping track of patients within a large hospital, and the state of our process is the specific department housing the patient. If we record the patient’s location e

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Queueing Processes,eing processes are those stochastic processes arising from waiting line phenomena. For example, the modeling of the arrival process of grain trucks to an elevator, the utilization of data processing services at a computer center, and the flow of jobs at a job shop facility all involve waiting lines.

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Event-Driven Simulation and Output Analyses, powerful because it can be used to model a wide diversity of situations, and it may be dangerous because its relative simplicity may lead to misuse and misinterpretation of results. However, with good training in the use of simulation and careful statistical analysis, discrete simulation can be use

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Markov Decision Processes,c that allows for a Markov model is a probability law in which the future behavior of the system is independent of the past behavior given the present condition of the system. When this Markov property is present, the dynamics of the process can be described by a matrix containing the one-step trans

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Advanced Queues,h the use of queues. However, a major drawback to the application of the queueing theory discussed so far is its dependence on the Poisson and exponential assumptions regarding the probability laws for the arrival and service processes. One of the properties of the exponential distribution is that i

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