Journal
NEUROIMAGE
Volume 112, Issue -, Pages 375-381Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2015.03.008
Keywords
-
Funding
- Wellcome Trust [091593/Z/10/Z]
- Office of Science of the U.S. Department of Energy [DE-AC05-00OR22725]
- EPSRC [EP/I017909/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/I017909/1] Funding Source: researchfish
Ask authors/readers for more resources
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). (C) 2015 The Authors. Published by Elsevier Inc.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available