Imaging Inverse Problems and Bayesian Computation - Python tutorials to learn about (accelerated) sampling for uncertainty quantification and other advanced inferences
Implementation of hierarchical Bayesian longitudinal models to estimate differential equation parameters.
Source code for Bayesian urban inversion OSSE [Kunik et al., 2019] written in R and equipped with sample inputs (run it out-of-the-box!)
This contains the codes used in the paper 'A randomized Multi-index sequential Monte Carlo method'.
A small collection of python-scripts associated with Gaussian process emulators in Bayesian inverse problems
Stochastic modelling of urban travel demand
Official code and reproducibility suite for 'Parameter-Space Error Localization and Posterior Fidelity of Physics-Informed Neural Network Forward Surrogates in Bayesian Inverse Problems' — multi-PDE benchmarks, optimal transport bounds, and MCMC calibration.
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