2011/12/18 by Betancourt, Michael, Stein, Leo C.
#Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Methodology (stat.ME) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.1112.4118
With its systematic exploration of probability distributions, Hamiltonian Monte Carlo is a potent Markov Chain Monte Carlo technique; it is an approach, however, ultimately contingent on the choice of a suitable Hamiltonian function. By examining both the symplectic geometry underlying Hamiltonian dynamics and the requirements of Markov Chain Monte Carlo, we construct the general form of admissible Hamiltonians and propose a particular choice with potential application in Bayesian inference.