HMCF - Hamiltonian Monte Carlo Sampling for Fields - A Python framework for HMC sampling with NIFTy
Abstract
HMCF "Hamiltonian Monte Carlo for Fields" is a software add-on for the NIFTy "Numerical Information Field Theory" framework implementing Hamiltonian Monte Carlo (HMC) sampling in Python. HMCF as well as NIFTy are designed to address inference problems in high-dimensional spatially correlated setups such as image reconstruction. HMCF adds an HMC sampler to NIFTy that automatically adjusts the many free parameters steering the HMC sampling machinery. A wide variety of features ensure efficient full-posterior sampling for high-dimensional inference problems. These features include integration step size adjustment, evaluation of the mass matrix, convergence diagnostics, higher order symplectic integration and simultaneous sampling of parameters and hyperparameters in Bayesian hierarchical models.
Cite
@article{arxiv.1807.02709,
title = {HMCF - Hamiltonian Monte Carlo Sampling for Fields - A Python framework for HMC sampling with NIFTy},
author = {Christoph Lienhard and Torsten A. Enßlin},
journal= {arXiv preprint arXiv:1807.02709},
year = {2018}
}
Comments
36 pages, 4 figures, available at https://gitlab.mpcdf.mpg.de/ift/HMCF, see also arXiv:1708.01073