Bernoulli

  • Bernoulli
  • Volume 2, Number 4 (1996), 341-363.

Exponential convergence of Langevin distributions and their discrete approximations

Gareth O. Roberts and Richard L. Tweedie

Full-text: Open access

Abstract

In this paper we consider a continuous-time method of approximating a given distribution using the Langevin diffusion . We find conditions under which this diffusion converges exponentially quickly to or does not: in one dimension, these are essentially that for distributions with exponential tails of the form , , exponential convergence occurs if and only if . We then consider conditions under which the discrete approximations to the diffusion converge. We first show that even when the diffusion itself converges, naive discretizations need not do so. We then consider a 'Metropolis-adjusted' version of the algorithm, and find conditions under which this also converges at an exponential rate: perhaps surprisingly, even the Metropolized version need not converge exponentially fast even if the diffusion does. We briefly discuss a truncated form of the algorithm which, in practice, should avoid the difficulties of the other forms.

Article information

Source
Bernoulli Volume 2, Number 4 (1996), 341-363.

Dates
First available in Project Euclid: 4 May 2007

Permanent link to this document
https://projecteuclid.org/euclid.bj/1178291835

Mathematical Reviews number (MathSciNet)
MR1440273

Zentralblatt MATH identifier
0870.60027

Keywords
diffusions discrete approximations geometric ergodicity Hastings algorithms irreducible Markov processes Langevin models Markov chain Monte Carlo Metropolis algorithms posterior distributions

Citation

Roberts, Gareth O.; Tweedie, Richard L. Exponential convergence of Langevin distributions and their discrete approximations. Bernoulli 2 (1996), no. 4, 341--363. https://projecteuclid.org/euclid.bj/1178291835.


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