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Optimal scaling of discrete approximations to Langevin diffusions

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We consider the optimal scaling problem for proposal distributions in Hastings–Metropolis algorithms derived from Langevin diffusions. We prove an asymptotic diffusion limit theorem and show that the relative efficiency of the algorithm can be characterized by its overall acceptance rate, independently of the target distribution. The asymptotically optimal acceptance rate is 0.574. We show that, as a function of dimension n, the complexity of the algorithm is O(n1/3), which compares favourably with the O(n) complexity of random walk Metropolis algorithms. We illustrate this comparison with some example simulations.
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Keywords: Hastings–Metropolis algorithm; Langevin algorithm; Markov chain Monte Carlo method; Weak convergence

Document Type: Original Article

Affiliations: 1: University of Cambridge, UK, 2: University of Toronto, Canada

Publication date: 1998-01-01

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