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Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
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JBris/model-calibration-evaluation
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Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Seeconfig.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
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Evaluating model calibration methods for sensitivity analysis, uncertainty analysis, optimisation, and Bayesian inference
Topics
deep-learning pymc global-optimization sensitivity-analysis bayesian-optimization bayesian-statistics kriging uncertainty-analysis approximate-bayesian-computation polynomial-chaos-expansion surrogate-models generative-neural-network optuna likelihood-free-inference simulation-based-inference sobol-indices shuffled-complex-evolution differential-evolution-mcmc polynomial-chaos bolfi
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