Multi-Point Metropolis Method with Application to 
Hybrid Monte Carlo


Z.S. Qin and J.S. Liu. J. Comp. Phys., 172:827-40.

ABSTRACT

We propose the multi-point Metropolis algorithm as an
extension of the orientational-bias Monte Carlo of
Frenkel and Smit. A ratio statistics similar to that in 
the Metropolis algorithm is  introduced to maintain the 
detailed balance. The  multi-point idea can  be applied  
to improve the efficiency of a general Markov chain-based 
Monte Carlo algorithm. To illustrate, we describe two 
variations of the idea, the random-grid Metropolis and 
the multi-point Hybrid Monte Carlo, and apply them to 
a number of examples.

KEYWORDS

Boltzmann distribution; Gibbs sampler; Hamiltonian; 
Heat-bath algorithm; Leap-frog; Mixture Gaussian; 
Uncoupled oscillator.