Imaging Inverse Problems and Bayesian Computation - Python tutorials to learn about (accelerated) sampling for uncertainty quantification and other advanced inferences
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Updated
Jan 12, 2024 - Jupyter Notebook
Imaging Inverse Problems and Bayesian Computation - Python tutorials to learn about (accelerated) sampling for uncertainty quantification and other advanced inferences
MATLAB package that models physical phenomena and performs uncertainty quantification on parameters of interest for coupled elastic-acoustic quantitative photoacoustic tomography
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!)
Implementation of hierarchical Bayesian longitudinal models to estimate differential equation parameters.
A small collection of python-scripts associated with Gaussian process emulators in Bayesian inverse problems
This contains the codes used in the paper 'A randomized Multi-index sequential Monte Carlo method'.
Stochastic modelling of urban travel demand
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