Modelling with non-stratified chain event graphs
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Series
Springer Proceedings in Mathematics & Statistics;
Volume
296
Pagination
155 - 163
ISBN-13
9783030306106
DOI
10.1007/978-3-030-30611-3_16
ISSN
2194-1009
Metadata
Show full item recordAbstract
© 2019, Springer Nature Switzerland AG. Chain Event Graphs (CEGs) are recent probabilistic graphical modelling tools that have proved successful in modelling scenarios with context-specific independencies. Although the theory underlying CEGs supports appropriate representation of structural zeroes, the literature so far does not provide an adaptation of the vanilla CEG methods for a real-world application presenting structural zeroes also known as the non-stratified CEG class. To illustrate these methods, we present a non-stratified CEG representing a public health intervention designed to reduce the risk and rate of falling in the elderly. We then compare the CEG model to the more conventional Bayesian Network model when applied to this setting.