We propose a Bayesian method to detect change points in sequence of functional observations that are signal functions observed with noises. Since have unlimited features, it is natural think the driving underlying through an evolution process, is, different features over time but possibly at times. A change-point then viewed as cumulative effect changes many so dissimilarities before and after ...

This paper addresses the problem of change-point detection in sequences high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to dimensionality problem, when time between change points is order dimension model parameters, drifts underlying distribution can be misidentified as changes. To overcome this limitation, we assume that observations lie l...

identification of a real time of a change in a process, when an out-of-control signal is present is significant. this may reduce costs of defective products as well as the time of exploring and fixing the cause of defects. another popular topic in the statistical process control (spc) is profile monitoring, where knowing the distribution of one or more quality characteristics may not be appropr...