The Hybrid (also called Hamiltonian) Monte Carlo produces samples from distribution P using a set of Hamiltonian equations to guide the sampling process. It uses the negative of the log density as a potential energy, and a randomly generated momentum to set up a Hamiltonian system, which is then used to sample the distribution. This can result in a faster convergence than the random walk Metropolis sampler ( ).