Gradient-free MCMC methods for dynamic causal modelling

Journal Article · Fri May 01 00:00:00 EDT 2015 · NeuroImage

DOI:https://doi.org/10.1016/j.neuroimage.2015.03.008· OSTI ID:1241923

Sengupta, Biswa; Friston, Karl J.; Penny, Will D.

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: Elsevier
• Country of Publication: United States
• Language: English

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