Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference

See config.yaml for the ground-truth simulation parameters.

The following model calibration methods have been evaluated.

• Approximate Bayesian Computation - Sequential Monte Carlo
• Bayesian Optimisation
• Bayesian Optimisation for Likelihood-Free Inference
• Differential Evolution Adaptive Metropolis
• Experimental Design via Gaussian Process Emulation
• Flow Matching Posterior Estimation
• Genetic Algorithm
• Tree-structured Parzen Estimator
• Polynomial Chaos Expansion
• Polynomial Chaos Kriging
• Sparse Axis-Aligned Subspace Bayesian Optimization
• Shuffled Complex Evolution Algorithm Uncertainty Analysis
• Sequential Neural Posterior Estimation
• Sobol Sensitivity Analysis
• Truncated Marginal Neural Ratio Estimation