title: Gradient-free MCMC methods for dynamic causal modelling.
abstract: In this technical note we compare the performance of four gradient-free MCMC samplers (random walk Metropolis sampling, slice-sampling, adaptive MCMC sampling and population-based MCMC sampling with tempering) in terms of the number of independent samples they can produce per unit computational time. For the Bayesian inversion of a single-node neural mass model, both adaptive and population-based samplers are more efficient compared with random walk Metropolis sampler or slice-sampling; yet adaptive MCMC sampling is more promising in terms of compute time. Slice-sampling yields the highest number of independent samples from the target density - albeit at almost 1000% increase in computational time, in comparison to the most efficient algorithm (i.e., the adaptive MCMC sampler).
authors.affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. Electronic address: b.sengupta@ucl.ac.uk.
authors.forename: Biswa
authors.initials: B
authors.lastname: Sengupta
authors.affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. Electronic address: k.friston@ucl.ac.uk.
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
authors.affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. Electronic address: w.penny@ucl.ac.uk.
authors.forename: Will D
authors.initials: WD
authors.lastname: Penny
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data*; D008390:Markov Chains; D008959:Models, Neurological; D009010:Monte Carlo Method; D012984:Software; D016138:Walking / Q000502:physiology