Hamiltonian Monte Carlo (HMC) sampling method.
The original paper, that introduced this method is described in:
- Simon Duane, Anthony D. Kennedy, Brian J. Pendleton and Duncan Roweth (1987). "Hybrid Monte Carlo". Physics Letters B. 195 (2): 216–222.
Several implementation details are given in:
- Radford M. Neal (1996). "Monte Carlo Implementation". Bayesian Learning for Neural Networks. Springer. pp. 55–98.
The generalized sampling approach is described in:
- Francis J. Alexander, Gregory L. Eyink and Juan M. Restrepo (2005). "Accelerated Monte Carlo for Optimal Estimation of Time Series", Journal of Statistical Physics, vol.119, pp: 1331-1345.
To ensure smooth execution please install the required modules with:
$ pip install -r requirements.txt
Some example on how to use this method can be found below: