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Integrating Expert Knowledge with Data in Bayesian Networks: Preserving Data-Driven Expectations when the Expert
Anthony Costa Constantinou1, Norman Fenton2, Martin Neil3
1Risk and Information Management (RIM) Research Group, School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK, E1 4NS.
Expert Systems with Applications
|July 6, 2016
Summary
This study presents a method for integrating expert judgment into Bayesian networks (BNs) when data is limited. It ensures expert variables preserve data distributions, crucial for accurate decision analysis with limited historical data.
Area of Science:
- Artificial Intelligence
- Statistics
- Decision Analysis
Background:
- Bayesian networks (BNs) often require expert knowledge for factors lacking data.
- Integrating expert judgment with existing data distributions in BNs is challenging.
- Existing methods may not preserve data variable distributions when incorporating new expert factors.
Purpose of the Study:
- To develop a method for incorporating expert variables into Bayesian networks (BNs) without altering the distribution of existing data variables.
- To ensure that the expected values of data variables are preserved when expert judgment is introduced.
- To provide a framework for assessing the accuracy of expert judgment in minimizing variability.
Main Methods:
- Developed a novel method for eliciting expert judgment to be integrated into a BN.
- Proposed a technique to ensure the expected values of a continuous data variable are preserved.
- Introduced a metric to quantify the accuracy of expert input by minimizing distribution variability.
- Outlined a procedure for incorporating assessments of rare or unobserved events.
Main Results:
- The proposed method successfully preserves the expected values of data variables when expert variables are unobserved.
- It was demonstrated that preserving the variance of data variables is generally not feasible or realistic.
- A method was established to assess expertise accuracy by minimizing revised empirical distribution variability.
- The study provides guidance on integrating assessments of rare events.
Conclusions:
- The developed method offers a robust way to integrate expert knowledge into Bayesian networks while preserving key statistical properties of existing data.
- Accurate elicitation of expert judgment is crucial for enhancing probabilistic models.
- The approach facilitates the modeling of complex systems with both data-driven and expert-informed variables, including rare events.
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