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Gradient-free MCMC methods for dynamic causal modelling.
- Source :
-
NeuroImage [Neuroimage] 2015 May 15; Vol. 112, pp. 375-381. Date of Electronic Publication: 2015 Mar 14. - Publication Year :
- 2015
-
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).<br /> (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 1095-9572
- Volume :
- 112
- Database :
- MEDLINE
- Journal :
- NeuroImage
- Publication Type :
- Academic Journal
- Accession number :
- 25776212
- Full Text :
- https://doi.org/10.1016/j.neuroimage.2015.03.008