A Bayesian network interpretation of the Cox's proportional hazard model.
International Journal of Approximate Reasoning(2018)
摘要
Cox's proportional hazards (CPH) model is quite likely the most popular modeling technique in survival analysis. While the CPH model is able to represent a relationship between a collection of risks and their common effect, Bayesian networks have become an attractive alternative with an increased modeling power and far broader applications. Our paper focuses on a Bayesian network interpretation of the CPH model (BN-Cox). We provide a method of encoding knowledge from existing CPH models in the process of knowledge engineering for Bayesian networks. This is important because in practice we often have CPH models available in the literature and no access to the original data from which they have been derived.
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关键词
Bayesian network,Cox's proportional hazard model
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