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).
Original language | English |
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Pages (from-to) | 375-381 |
Number of pages | 7 |
Journal | NeuroImage |
Volume | 112 |
Early online date | 14 Mar 2015 |
DOIs | |
Publication status | Published - 15 May 2015 |
Keywords
- Algorithms
- Bayes Theorem
- Humans
- Computer-Assisted Image Processing
- Markov Chains
- Neurological Models
- Monte Carlo Method
- Software
- Walking
Profiles
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William Penny
- School of Psychology - Professor in Psychology
Person: Research Group Member, Academic, Teaching & Research