Optimal tuning of the hybrid Monte Carlo algorithm

@article{Beskos2010OptimalTO,
  title={Optimal tuning of the hybrid Monte Carlo algorithm},
  author={Alexandros Beskos and Natesh S. Pillai and Gareth O. Roberts and Jes{\'u}s Mar{\'i}a Sanz-Serna and Andrew M. Stuart},
  journal={Bernoulli},
  year={2010},
  volume={19},
  pages={1501-1534}
}
We investigate the properties of the Hybrid Monte Carlo algorithm (HMC) in high dimensions. HMC develops a Markov chain reversible w.r.t. a given target distribution . by using separable Hamiltonian dynamics with potential -log .. The additional momentum variables are chosen at random from the Boltzmann distribution and the continuous-time Hamiltonian dynamics are then discretised using the leapfrog scheme. The induced bias is removed via a Metropolis- Hastings accept/reject rule. In the… 
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