Gradient-free MCMC methods for dynamic causal modelling
Here, 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).
- Research Organization:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF); Univ. College London (United Kingdom)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- Grant/Contract Number:
- AC05-00OR22725
- OSTI ID:
- 1241923
- Alternate ID(s):
- OSTI ID: 1344386
- Journal Information:
- NeuroImage, Journal Name: NeuroImage Vol. 112 Journal Issue: C; ISSN 1053-8119
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
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