============================================== pyhmc: Hybrid Monte Carlo Sampling with python ============================================== This package is a straight-forward port of the functions hmc2.m and hmc2_opt.m from the MCMCstuff matlab toolbox written by Aki Vehtari <http://www.lce.hut.fi/research/mm/mcmcstuff/>. The code is originally based on the functions hmc.m from the netlab toolbox written by Ian T Nabney <http://www.ncrg.aston.ac.uk/netlab/index.php>. The portion of algorithm involving "windows" is derived from the C code for this function included in the Software for Flexible Bayesian Modeling written by Radford Neal <http://www.cs.toronto.edu/~radford/fbm.software.html>. This software is distributed under the BSD License (see LICENSE file). Authors ------- - Kilian Koepsell <firstname.lastname@example.org> Contents -------- * README: This file. * LICENSE: The files in this directory are distributed under the BSD license. * hmc2.py: The main module with Hybrid Monte Carlo sampler. This is the only file needed, which can serve as a drop-in replacement of hmc2.m. Run some tests: 'python hm2.py' * hmc2x.pyx: If cython is installed, this file will be used to speed up the main loop. * f_energy.py: Some example energy functions with gradients. * f_energyx.pyx: Cython version of some example energy functions with gradients. * test_hmc2.py: A couple of tests. (requires nose) * hmc.m, hmc2.m, hmc2_opt.m: Original matlab version as a reference.