Gaussian Process Bayesian Toolkit with Monte Carlo Sampler Integration for Heavy Ion Collisions
This toolkit implements a wrapper for Gaussian Process (GP) emulators and Monte Carlo (MC) samplers used in high-energy heavy-ion simulations.
The following wrappers for GP emulators are currently included:
- Scikit Learn GP emulator wrapper
- PCGP and PCSK wrapper for the GPs implemented in the surmise package of the BAND Collaboration
The following wrappers for MC sampling are included:
- MCMC wrapper for the emcee package
- PTMCMC (Parallel Tempering Markov Chain Monte Carlo) wrapper
- Not recommend to use this one for larger runs. There are problems with the parallelization.
- PTLMC from the surmise package (Parallel Tempering Langevin Monte Carlo)
- pocoMC Preconditioned Monte Carlo method for accelerated Bayesian inference
❗ The jupyter notebooks are just meant as examples for how to use the emulators and samplers and analyze the output. Paths and data files need the proper input formats.