Constructing Metropolis-Hastings proposals using damped BFGS updates⁎. Issue 15 (2018)

• Record Type: Journal Article
• Authors: Dahlin, Johan; Wills, Adrian; Ninness, Brett
• Is Part Of: IFAC-PapersOnLine. Volume 51:Issue 15(2018)
• Page Start: 664
• Page End: 669
• Publication Date: 2018
• Subjects: Bayesian parameter inference -- state-space models -- quasi-Newton -- BFGS; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2018.09.208
• Languages: English
• ISSNs: 2405-8963

Abstract

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 identifcation. 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.