ABSTRACT
This chapter introduces Bayesian inferences for two types of stochastic processes, queues and time series. It provides that the process to be studied is the M/M/1 queue, and more complex queues. The chapter presents time series models, including the autoregressive, the moving average, the autoregressive moving average processes, and the regression models with residuals that are correlated time series. It describes the fundamental properties of queues, including the interarrival time of customers, the service times of the servers for the customers, and the traffic intensity. Bayesian inferences consist of three phases: estimation, testing hypotheses, and prediction of future observations. It is important to remember that Bayesian inferences depend on prior information about the unknown parameters, the sample information expressed by the likelihood function, and the resulting posterior distribution about those parameters. Generally speaking, a queuing system is a family of several stochastic processes describing the waiting and service times of the people in the queue.
