Major Advances and Emerging Developments of Graphical Models [From the Guest Editors]
Michael I. Jordan, Erik B. Sudderth, Martin J. Wainwright, Alan S. Willsky · IEEE Signal Processing Magazine · 2010
Graphical models, referred to in various guises as Markov random fields (MRFs), Bayesian networks, factor graphs, influence diagrams, decision networks, or structured stochastic systems, are a powerful and elegant marriage of graph theory, probability theory, and decision theory. They yield a unifying perspective on many long-standing and emerging frameworks for modeling complex phenomena, as well as methods for processing complex sources of data and signals. Such models are of particular importance in areas of signal processing that overlap with machine learning, time-series analysis, spatial statistics, and optimization.