damingondiwa / bayesianode-priorimpactassessment

This repository has code for the paper Bayesian prior impact assessment for dynamical systems described by ordinary differential equations

Wasserstein distance prior impact assessment for ODE models

Introduction

This repository contains supporting code for the pre-print:

Mingo, D. N., Hale, J. S. and Ley, C.,: Bayesian prior impact assessment for dynamical systems described by ordinary differential equations.

TODO: Add link to preprint

The code is archived at:

Mingo, D. N. and Hale, J. S.: Wasserstein distance prior impact assessment for ODE models, https://doi.org/10.5281/zenodo.11553775, 2024.

The code in this repository is licensed under the GNU Lesser General Public License version 3 or later, see COPYING and COPYING.LESSER.

Examples

Lotka-Volterra

Scripts to reproduce the results for the Lotka-Volterra example are contained in examples/lotka_volterra.

In order, run:

1. wasser_exlot.py
2. wasser_dist_prior.py
3. wasser_dist_ex.py

The scripts lotka_priors_ppc.py and pairplot.py can be executed independently.

SEIR

Scripts to reproduce the results for the SEIR example are contained in examples/SEIR.

In order, run:

1. All files in prior_samples/.
2. run_seirpost.sh executes wasser_seir.py and saves the samples.
3. wasser_seir.py
4. wd_mar_ex.py

Prior samples (SEIR)

The subfolder examples/SEIR/prior_samples contains scripts for sampling from prior distributions. Running these scripts on an HPC is preferable as they take longer, or use the script sample_batches.py for batch sampling to reduce execution time.

Additional scripts

The script example_diagnostics.py demonstrates how to perform Geweke diagnostics to check for model convergence. The script requires uploading posterior samples and specifying the model (SEIR or Lotka-Volterra).