Gradient-free MCMC methods for dynamic causal modelling

Sengupta, Biswa, Friston, Karl J and Penny, Will D. (2015) Gradient-free MCMC methods for dynamic causal modelling. NeuroImage, 112. pp. 375-381. ISSN 1053-8119

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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).

Item Type: Article
Additional Information: Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
Uncontrolled Keywords: algorithms,bayes theorem,humans,computer-assisted image processing,markov chains,neurological models,monte carlo method,software,walking
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 19 Aug 2017 05:06
Last Modified: 25 Jul 2019 00:08
URI: https://ueaeprints.uea.ac.uk/id/eprint/64580
DOI: 10.1016/j.neuroimage.2015.03.008

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