AD-HMC achieves geometric convergence in Wasserstein distance for HMC with general asymmetrical auxiliary momentum distributions by restoring self-adjointness via direction alternation, with extensions to leapfrog integrators.
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Parallel Picard-map algorithms for zeroth-order Random Walk Metropolis achieve O(sqrt(d)) parallel iterations with O(sqrt(d)) processors on log-concave distributions in d dimensions.
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Hamiltonian Monte Carlo with Asymmetrical Momentum Distributions
AD-HMC achieves geometric convergence in Wasserstein distance for HMC with general asymmetrical auxiliary momentum distributions by restoring self-adjointness via direction alternation, with extensions to leapfrog integrators.
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Parallel computations for Metropolis Markov chains with Picard maps
Parallel Picard-map algorithms for zeroth-order Random Walk Metropolis achieve O(sqrt(d)) parallel iterations with O(sqrt(d)) processors on log-concave distributions in d dimensions.