Gradient-free MCMC methods for dynamic causal modelling

Biswa Sengupta, Karl J Friston, Will D. Penny

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


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).

Original languageEnglish
Pages (from-to)375-381
Number of pages7
Early online date14 Mar 2015
Publication statusPublished - 15 May 2015


  • Algorithms
  • Bayes Theorem
  • Humans
  • Computer-Assisted Image Processing
  • Markov Chains
  • Neurological Models
  • Monte Carlo Method
  • Software
  • Walking

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