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Constructing Metropolis-Hastings proposals using damped BFGS updates

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  • Johan Dahlin
  • Adrian Wills
  • Brett Ninness

Abstract

The computation of Bayesian estimates of system parameters and functions of them on the basis of observed system performance data is a common problem within system identification. This is a previously studied issue where stochastic simulation approaches have been examined using the popular Metropolis--Hastings (MH) algorithm. This prior study has identified a recognised difficulty of tuning the {proposal distribution so that the MH method provides realisations with sufficient mixing to deliver efficient convergence. This paper proposes and empirically examines a method of tuning the proposal using ideas borrowed from the numerical optimisation literature around efficient computation of Hessians so that gradient and curvature information of the target posterior can be incorporated in the proposal.

Suggested Citation

  • Johan Dahlin & Adrian Wills & Brett Ninness, 2018. "Constructing Metropolis-Hastings proposals using damped BFGS updates," Papers 1801.01243, arXiv.org, revised May 2018.
    • Handle: RePEc:arx:papers:1801.01243
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    References listed on IDEAS

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    1. A. Doucet & M. K. Pitt & G. Deligiannidis & R. Kohn, 2015. "Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator," Biometrika, Biometrika Trust, vol. 102(2), pages 295-313.
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