1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.ME2020
The FMRIB Variational Bayesian Inference Tutorial II: Stochastic Variational Bayes
Michael A. Chappell, Mark W. Woolrich
Bayesian methods have proved powerful in many applications for the inference of model parameters from data. These methods are based on Bayes' theorem, which itself is deceptively s…
eess.SP2020★ 1 cited
Stochastic Variational Bayesian Inference for a Nonlinear Forward Model
Michael A. Chappell, Martin S. Craig, Mark W. Woolrich
Variational Bayes (VB) has been used to facilitate the calculation of the posterior distribution in the context of Bayesian inference of the parameters of nonlinear models from dat…
q-bio.NC2016
Resting state brain networks from EEG: Hidden Markov states vs. classical microstates
Tammo Rukat, Adam Baker, Andrew Quinn +1
Functional brain networks exhibit dynamics on the sub-second temporal scale and are often assumed to embody the physiological substrate of cognitive processes. Here we analyse the…