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Using Almost-Dynamic Bayesian Networks to Represent Uncertainty in Complex Epidemiological Models: a Proposal
Last modified: 2018-05-18
Abstract
We introduce a dynamic model to deal with uncertainty in complex epidemiological processes. Our proposal is based on the Dynamic Bayesian Networks formalism, where each node is associated a random variable, whose value specifies the state of an individual from a given population.
References
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belief networks and influence diagrams). In Proceedings of the Fourteenth conference on Uncertainty
in artificial intelligence, pages 480–487. Morgan Kaufmann Publishers Inc., 1998.
control, volume 28. Wiley Online Library, 1992.
[2] Tom Britton. Stochastic epidemic models: a survey. Mathematical biosciences, 225(1):24–35, 2010.
[3] Volker Grimm and Steven F Railsback. Individual-based modeling and ecology:(princeton series in
theoretical and computational biology). 2005.
[4] Matt J Keeling and Pejman Rohani. Modeling infectious diseases in humans and animals. Princeton
University Press, 2011.
[5] W Kermack and A McKendrick. A contribution to the mathematical theory of epidemics. In Proc. Roy.
Soc. Lond, pages 700–721, 1927.
[6] Kevin Patrick Murphy. Dynamic bayesian networks: representation, inference and learning. 2002.
[7] Judea Pearl. Probabilistic reasoning in intelligent systems: networks of plausible inference. Elsevier,
2014.
[8] Ross D Shachter. Bayes-ball: Rational pastime (for determining irrelevance and requisite information in
belief networks and influence diagrams). In Proceedings of the Fourteenth conference on Uncertainty
in artificial intelligence, pages 480–487. Morgan Kaufmann Publishers Inc., 1998.
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