BUQ-ODEs

The title of the repository stands for Bayesian Uncertainty Quantification in Ordinary Differential Equations.

The purpose of this repository is to provide the examples of the MCMC methods tested in my thesis Stochastic Simulation in Multimodal Posteriors: UQ in ODEs, CIMAT 2020. Programs are coded in Python 3. Additional purposes include providing examples of parameter estimation in ordinary differential equations (ODEs) and multimodal posterior distributions. The methods provided use libraries and packages already developed. The methods being exemplified are

1. t walk (Christen, Fox 2010)
2. emcee (Goodman, Weare 2010)
3. Hamiltonian MC (Stan - )
4. Parallel Tempering MC with a Hamiltonian kernel
5. Random scan Gibbs sampling

Pending: Describe the files. I might do this someday. If you are curious about anything, you can contact me at javier dot aguilar at cimat dot mx (javier.aguilar@cimat.mx)