Integrating expert knowledge with data in Bayesian networks: Preserving data-driven expectations when the expert variables remain unobserved

作者：

Highlights：

• A method for integrating expert knowledge with data in Bayesian networks.

• Preserves the data-driven expectations when the expert variables remain unobserved.

• Supports the incorporation of extremely rare or previously unobserved events.

• The method extends towards determining the accuracy of expertise.

摘要

•A method for integrating expert knowledge with data in Bayesian networks.•Preserves the data-driven expectations when the expert variables remain unobserved.•Supports the incorporation of extremely rare or previously unobserved events.•The method extends towards determining the accuracy of expertise.

论文关键词：Bayesian networks,Belief networks,Causal inference,Expert knowledge,Knowledge elicitation,Probabilistic graphical models

论文评审过程：Received 8 November 2015, Revised 26 February 2016, Accepted 29 February 2016, Available online 18 March 2016, Version of Record 28 March 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.02.050