Gradient-free MCMC methods for dynamic causal modelling

NeuroImage
Biswa SenguptaWill D Penny

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).

References

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Citations

Jul 28, 2015·NeuroImage·Biswa SenguptaWill D Penny
Nov 17, 2015·NeuroImage·Karl J FristonPeter Zeidman
Sep 20, 2015·Journal of Neuroscience Methods·Eduardo A AponteJakob Heinzle
Apr 27, 2016·NeuroImage·Dirk Ostwald, Ludger Starke
May 5, 2016·Journal of Neuroscience Methods·Sudhir RamanKlaas Enno Stephan
Mar 18, 2015·NeuroImage·Biswa SenguptaWill D Penny
Jan 26, 2018·Wiley Interdisciplinary Reviews. Cognitive Science·Stefan FrässleKlaas E Stephan
Mar 5, 2016·PLoS Computational Biology·Will Penny, Biswa Sengupta
May 11, 2021·Wellcome Open Research·Karl J FristonChristian Lambert
Jun 22, 2021·Frontiers in Psychiatry·Stefan FrässleKlaas E Stephan

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Software Mentioned

Matlab
CGStab
Parallel Computing Toolbox
CVODES
Bi

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