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Found 7 papers, 3 papers with code
Non-reversibly updating a uniform [0,1] value for Metropolis accept/reject decisions
I demonstrate that for a problem with some continuous variables, updated by HMC or Langevin updates, and also discrete variables, updated by Gibbs sampling between updates of the continuous variables, Langevin with persistent momentum and non-reversible updates to u samples nearly a factor of two more efficiently than HMC.
Non-reversibly updating a uniform [0,1] value for accept/reject decisions
I show how it can be beneficial to express Metropolis accept/reject decisions in terms of comparison with a uniform [0, 1] value, and to then update this uniform value non-reversibly, as part of the Markov chain state, rather than sampling it independently each iteration.
Fast exact summation using small and large superaccumulators
For big summations, a "large" superaccumulator is used as well.
Numerical Analysis Distributed, Parallel, and Cluster Computing Computation G.1.0
Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals
We compare our model, using synthetic datasets, with a model proposed by Goldberg, Williams and Bishop (1998), which we refer to as GPLV, which only deals with case (a), as well as a standard GP model which can handle only case (c).
MCMC using Hamiltonian dynamics
Hamiltonian dynamics can be used to produce distant proposals for the Metropolis algorithm, thereby avoiding the slow exploration of the state space that results from the diffusive behaviour of simple random-walk proposals.
Computation Computational Physics
Split Hamiltonian Monte Carlo
With the splitting technique, only the slowly-varying part of the energy needs to be handled numerically, and this can be done with a larger stepsize (and hence fewer steps) than would be necessary with a direct simulation of the dynamics.
Computation
Connectionist Learning of Belief Networks
Connectionist learning procedures are presented for "sigmoid" and "noisy-OR" varieties of probabilistic belief networks.
