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ToolsSengupta, 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
Full text not available from this repository. (Request a copy)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 |
| UEA Research Groups: | Faculty of Social Sciences > Research Centres > Centre for Behavioural and Experimental Social Sciences |
| Depositing User: | Pure Connector |
| Date Deposited: | 19 Aug 2017 05:06 |
| Last Modified: | 06 Feb 2025 07:22 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/64580 |
| DOI: | 10.1016/j.neuroimage.2015.03.008 |
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