In statistical process control (SPC) problems, in-control values of parameters are required by traditional approaches. However this requirement is not realistic. New methods based on the change point model have been developed to avoid this requirement. The existing change-point methods are restricted to independent identically distributed observations, ignoring the numerous settings in which process readings are serially correlated. Furthermore, these frequentist methods are unable to make use of prior imperfect</DISS_para>
<DISS_para>information on the parameters. In my research, I propose a Bayesian approach to the online SPC based on the change point model in an ARMA process. This approach accommodates serially correlated data, and also provides a coherent way of incorporating prior information on parameters.