Common quandaries and their practical solutions in Bayesian network modeling

- The increasing popularity of Bayesian network modeling carries risks of problems in model construction and interpretation.
- Construction problems include ill-defined variables, gaps in assigning probability values, and lack of validation and peer review.
- Interpretation problems include shifts in modeling objectives, confusions of causation, lack of rigor in use of expert knowledge; and more.
- Solutions are offered for each problem.

Use and popularity of Bayesian network (BN) modeling has greatly expanded in recent years, but many common problems remain. Here, I summarize key problems in BN model construction and interpretation, along with suggested practical solutions. Problems in BN model construction include parameterizing probability values, variable definition, complex network structures, latent and confounding variables, outlier expert judgments, variable correlation, model peer review, tests of calibration and validation, model overfitting, and modeling wicked problems. Problems in BN model interpretation include objective creep, misconstruing variable influence, conflating correlation with causation, conflating proportion and expectation with probability, and using expert opinion. Solutions are offered for each problem and researchers are urged to innovate and share further solutions.