/GPBayesTools-HIC

Gaussian Process Bayesian Toolkit with Monte Carlo Sampler Integration for Heavy Ion Collisions

Primary LanguageJupyter NotebookMIT LicenseMIT

GPBayesTools-HIC

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)

❗ 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.