BibTeX
@ARTICLE{
Sengupta2016GbM,
author = "Biswa Sengupta and Karl J. Friston and Will D. Penny",
title = "Gradientbased {MCMC} samplers for dynamic causal modelling",
journal = "NeuroImage",
volume = "125",
pages = "11071118",
year = "2016",
issn = "10538119",
doi = "10.1016/j.neuroimage.2015.07.043",
url = "http://www.sciencedirect.com/science/article/pii/S1053811915006540",
abstract = "In this technical note, we derive two MCMC (Markov chain Monte Carlo) samplers for
dynamic causal models (DCMs). Specifically, we use (a) Hamiltonian MCMC (HMCE) where sampling is
simulated using Hamilton's equation of motion and (b) Langevin Monte Carlo algorithm (LMCR and
LMCE) that simulates the Langevin diffusion of samples using gradients either on a Euclidean (E) or
on a Riemannian (R) manifold. While LMCR requires minimal tuning, the implementation of HMCE is
heavily dependent on its tuning parameters. These parameters are therefore optimised by learning a
Gaussian process model of the timenormalised sample correlation matrix. This allows one to
formulate an objective function that balances tuning parameter exploration and exploitation,
furnishing an interventionfree inference scheme. Using neural mass models (NMMs)a class of
biophysically motivated DCMswe find that HMCE is statistically more efficient than LMCR (with a
Riemannian metric); yet both gradientbased samplers are far superior to the random walk Metropolis
algorithm, which proves inadequate to steer away from dynamical instability.",
ad_area = "Neuroscience",
ad_tools = "ADiMat"
}
