Abstract
The growth of new sustainable technologies and processes requires fundamental systems modeling methodologies to assess and predict their performance. This article describes theoretical frameworks for complex network modeling for sustainability. It begins with basic Markovian models, including Markov chains and hidden and hierarchical hidden Markov models. It then moves to more advanced Bayesian methods, including Bayesian networks and dynamic Bayesian networks. Each of these frameworks is described, including their definitions, properties, and relevance to network modeling for sustainability. As probabilistic graphical frameworks, they are able to capture the complex interdependencies in sustainable systems, the interactions of components comprising any given system, and the intricate and often uncertain network dynamics for sustainable systems.
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