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ox.ac.uk
MCMC for Hierachical Bayesian Models Using Non-reversible Langevin Methods
Hamiltonian Monte Carlo (HMC) is an attractive MCMC method for continuous distributions because it makes use of the gradient of the log probability density to propose points far from the current point, avoiding slow exploration by a random walk (RW). The Langevin method - equivalent to HMC with one leapfrog step - also uses gradient information ...
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