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Sengupta, Biswa, Friston, Karl J and Penny, Will D. 
ORCID: https://orcid.org/0000-0001-9064-1191
(2016)
Gradient-based MCMC samplers for dynamic causal modelling.
NeuroImage, 125.
pp. 1107-1118.
ISSN 1053-8119
Abstract
In this technical note, we derive two MCMC (Markov chain Monte Carlo) samplers for dynamic causal models (DCMs). Specifically, we use (a) Hamiltonian MCMC (HMC-E) where sampling is simulated using Hamilton's equation of motion and (b) Langevin Monte Carlo algorithm (LMC-R and LMC-E) that simulates the Langevin diffusion of samples using gradients either on a Euclidean (E) or on a Riemannian (R) manifold. While LMC-R requires minimal tuning, the implementation of HMC-E is heavily dependent on its tuning parameters. These parameters are therefore optimised by learning a Gaussian process model of the time-normalised sample correlation matrix. This allows one to formulate an objective function that balances tuning parameter exploration and exploitation, furnishing an intervention-free inference scheme. Using neural mass models (NMMs)-a class of biophysically motivated DCMs-we find that HMC-E is statistically more efficient than LMC-R (with a Riemannian metric); yet both gradient-based samplers are far superior to the random walk Metropolis algorithm, which proves inadequate to steer away from dynamical instability.
| Item Type: | Article |
|---|---|
| Additional Information: | Copyright © 2015. Published by Elsevier Inc. |
| Uncontrolled Keywords: | algorithms,bayes theorem,humans,computer-assisted image interpretation,markov chains,theoretical models,monte carlo method,neuroimaging,comparative study |
| 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: | 18 Aug 2017 05:07 |
| Last Modified: | 20 Apr 2023 00:32 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/64568 |
| DOI: | 10.1016/j.neuroimage.2015.07.043 |
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