Bayesian Model Comparison

Compare different model specifications and prior choices to find the best approach

Select Models to Compare
Choose up to 3 models for detailed comparison
Model A: Weakly Informative Priors
Standard weakly informative approach, minimal prior influence
Bayesian8000 iterations
Model B: Expert Elicitation Priors
Incorporates expert knowledge from previous policy evaluations
Bayesian8000 iterations
Model C: Regularized Frequentist
Traditional frequentist approach with regularization
Frequentist1 iterations
Model Performance Metrics
Comparison across key evaluation criteria (0-1 scale)
Predictive AccuracyUncertainty QuantificationComputational EfficiencyInterpretabilityRobustness00.250.50.751
  • Model A
  • Model B
  • Model C
MetricModel AModel BModel C
Predictive Accuracy87%91%84%
Uncertainty Quantification85%88%72%
Computational Efficiency92%88%95%
Interpretability80%78%90%
Robustness83%89%81%
Recommended Model

Model B: Expert Elicitation Priors is recommended for government policy decisions because it:

  • ✓ Achieves highest predictive accuracy (91%)
  • ✓ Provides best uncertainty quantification
  • ✓ Incorporates domain expertise from previous evaluations
  • ✓ Maintains excellent convergence diagnostics
  • ✓ Balances accuracy with interpretability