Reviewing Graphical Modelling of Multivariate Temporal Processes

Graphical models provide a suitable approach of dealing with uncertainty and complexity by using conditional independence statements and factorizations of joint densities. Static undirected as well as directed graphical models have been applied frequently to pattern analysis, decision modelling, machine learning or image filtering. Several temporal extensions have been published including dynamic Bayesian networks or temporal Markov random fields. Although, graphical models are most commonly used within computer science there has been a growing interest in adjacent disciplines. Recently, a few temporal extensions have been applied to multivariate time series data and event histories.