Conjugate exponential family graphical models in process monitoring: A tutorial review

Over the past few decades, both academic and industrial communities have shown increasing interest in the use of data-driven models for process monitoring applications. There exist numerous data-driven techniques in the literature for process monitoring based on the models borrowed from fields such as chemometrics, statistics and different areas of machine learning. This paper reviews a class of probabilistic models, conjugate exponential family graphical models (CEFGMs) for process monitoring applications and also serves as a tutorial for two popular estimation algorithms for the CEFGMs, the expectation maximization (EM) and variational Bayesian expectation maximization (VBEM) algorithms.